Lidar to camera calibration for generating high definition maps

ABSTRACT

A system performs calibration of sensors mounted on a vehicle, for example, lidar and camera sensors mounted on a vehicle, for example, an autonomous vehicle. The system receives a lidar scan and camera image of a view and determines a lidar-to-camera transform based on the lidar scan and the camera image. The system may use a pattern, for example, a checkerboard pattern in the view for calibration. The pattern is placed close to the vehicle to determine an approximate lidar-to-camera transform and then placed at a distance from the vehicle to determine an accurate lidar-to-camera transform. Alternatively, the system determines edges in the lidar scan and the camera image and aligns features based on real-world objects in the scene by comparing edges.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority under 35 USC 119(e) toU.S. Provisional Application No. 62/574,744 entitled “Lidar to CameraCalibration for Generating High Definition Maps,” filed on Oct. 19,2017, which is incorporated herein by reference in its entirety for allpurposes.

BACKGROUND

This disclosure relates generally to calibration of sensors on vehicles,for example, autonomous vehicles, and more particularly to calibrationof lidar and camera sensors of installed on vehicles.

Autonomous vehicles, also known as self-driving cars, driverless cars,auto, or robotic cars, drive from a source location to a destinationlocation without requiring a human driver to control and navigate thevehicle. Automation of driving is difficult due to several reasons. Forexample, autonomous vehicles use sensors to make driving decisions onthe fly, but vehicle sensors cannot observe everything all the time.Vehicle sensors can be obscured by corners, rolling hills, and othervehicles. Vehicles sensors may not observe certain things early enoughto make decisions. In addition, lanes and signs may be missing on theroad or knocked over or hidden by bushes, and therefore not detectableby sensors. Furthermore, road signs for rights of way may not be readilyvisible for determining from where vehicles could be coming, or forswerving or moving out of a lane in an emergency or when there is astopped obstacle that must be passed.

Autonomous vehicles can use map data to figure out some of the aboveinformation instead of relying on sensor data. However conventional mapshave several drawbacks that make them difficult to use for an autonomousvehicle. For example maps do not provide the level of accuracy requiredfor safe navigation (e.g., 10 cm or less). GPS systems provideaccuracies of approximately 3-5 meters, but have large error conditionsresulting in an accuracy of over 100 m. This makes it challenging toaccurately determine the location of the vehicle.

Autonomous vehicles use various processes for self-driving based on highdefinition maps generated using data obtained from multiple sensors, forexample, lidar and camera sensors. Each sensor of the autonomousvehicle, may use its own coordinate system. For example, the lidar mayuse one coordinate system and a camera may use another coordinatesystem. If the coordinate systems used by two different sensors are notcalibrated together, any processing that combines data from the twosensors is likely to be inaccurate. Furthermore, the calibrationparameters of various sensors of autonomous vehicles drift over time.Conventional techniques require manual processing by experts, therebyrequiring autonomous vehicles to be provided to the experts forcalibration. Such techniques are time consuming and expensive.Furthermore, these techniques put burden on the users of the vehicles byrequiring them to arrive at a specialized facility for calibration or toperform technical steps on their own for performing calibration.

SUMMARY

Embodiments of the invention perform calibration of sensors mounted on avehicle, for example, lidar and camera sensors mounted on an autonomousvehicle.

A system receives a lidar scan of a view comprising a pattern, forexample, a checkerboard pattern captured by a lidar mounted on anautonomous vehicle. The pattern is positioned less that a thresholddistance from the autonomous vehicle. The system also receives a cameraimage of the view captured by a camera mounted on the autonomousvehicle. The system determines an approximate lidar-to-camera transformbased on the lidar scan and the camera image of the first view.

The system further receives a second lidar scan of a view comprising thepattern, such that the pattern is positioned greater than a thresholddistance from the autonomous vehicle. The system receives a secondcamera image of the view captured by a camera mounted on the autonomousvehicle. The system determines an accurate lidar-to-camera transformbased on the location of the pattern in the second lidar scan and thelocation of the pattern in the camera image of the second view. Thesystem receives sensor data comprising images received from the cameraand lidar scans from the lidar and generates a high definition map basedon the sensor data using the accurate lidar-to-camera transform. Thesystem stores the high definition map in a computer readable storagemedium for use in navigating the autonomous vehicle. In an embodiment,the system sends signals to the controls of the autonomous vehicle basedon the high definition map.

Embodiments of the invention allow calibration of sensors of vehicleswithout requiring extensive manual setup or expert help. As a result,sensors of vehicles can be calibrated on a regular basis. This allowsaccurate correlation of data obtained by different sensors for combiningthe data. Since high definition maps are generated by combining datacaptured by different sensors, embodiments of the invention improve thequality of maps generated as well as efficiency of generation of map.

The features and advantages described in this summary and the followingdetailed description are not all-inclusive. Many additional features andadvantages will be apparent to one of ordinary skill in the art in viewof the drawings, specification, and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

Figure (FIG. 1 shows the overall system environment of an HD map systeminteracting with multiple vehicle computing systems, according to anembodiment.

FIG. 2 shows the system architecture of a vehicle computing system,according to an embodiment.

FIG. 3 illustrates the various layers of instructions in the HD Map APIof a vehicle computing system, according to an embodiment.

FIG. 4 shows the system architecture of an HD map system, according toan embodiment.

FIG. 5 illustrates the components of an HD map, according to anembodiment.

FIGS. 6A-B illustrate geographical regions defined in an HD map,according to an embodiment.

FIG. 7 illustrates representations of lanes in an HD map, according toan embodiment.

FIGS. 8A-B illustrates lane elements and relations between lane elementsin an HD map, according to an embodiment.

FIG. 9 illustrates the system architecture of a sensor calibrationmodule, according to an embodiment.

FIG. 10(A) illustrates sensor data obtained from a scene comprising acheckerboard pattern held in front of a vehicle, according to anembodiment.

FIG. 10(B) illustrates sensor data obtained from a scene comprising apattern including different colored tapes, for example, alternating redand blue tapes, according to an embodiment.

FIG. 11 shows a flowchart illustrating the overall process oflidar-to-camera calibration, according to an embodiment.

FIG. 12 shows a flowchart illustrating the process of the first phase oflidar-to-camera calibration based on a close view of the checkerboard,according to an embodiment.

FIG. 13 shows a flowchart illustrating the process of the second phaseof lidar-to-camera calibration that determines an accuratelidar-to-camera transform based on a distant view of the checkerboard,according to an embodiment.

FIG. 14 shows a flowchart illustrating a process for detecting thecheckerboard pattern based on a use of a single camera, according to anembodiment.

FIG. 15 shows a flowchart illustrating the process of fitting boundarypoints and a normal on the checkerboard, according to an embodiment.

FIG. 16 shows a flowchart illustrating the process of refining thecheckerboard pattern using intensity data, according to an embodiment.

FIG. 17 shows a flowchart illustrating the process of selecting a stillframe, according to an embodiment.

FIG. 18A shows a test sequence based on a striped pattern according toan embodiment.

FIG. 18B shows sample debug images for a test sequence, according to anembodiment.

FIG. 19A shows a top-down view of a reflective tape pattern on theground, according to an embodiment.

FIG. 19B shows a front view of the reflective tape pattern on the wall,according to an embodiment.

FIG. 20 shows a flowchart illustrating the process of determining aplacement of the checkerboard pattern, according to an embodiment.

FIG. 21 illustrates the overall process for performing calibration ofsensors of a vehicle based on edgel detection, according to anembodiment.

FIG. 22 illustrates the process for processing the ground pointsseparate from the remaining points for performing calibration of sensorsof a vehicle based on edgel detection, according to an embodiment.

FIG. 23 illustrates the process of searching for an improved transformbased on an initial transform, according to an embodiment.

FIG. 24 illustrates an embodiment of a computing machine that can readinstructions from a machine-readable medium and execute the instructionsin a processor or controller.

The figures depict various embodiments of the present invention forpurposes of illustration only. One skilled in the art will readilyrecognize from the following discussion that alternative embodiments ofthe structures and methods illustrated herein may be employed withoutdeparting from the principles of the invention described herein.

DETAILED DESCRIPTION Overview

Embodiments of the invention maintain high definition (HD) mapscontaining up to date information using high precision. The HD maps maybe used by autonomous vehicles to safely navigate to their destinationswithout human input or with limited human input. An autonomous vehicleis a vehicle capable of sensing its environment and navigating withouthuman input. Autonomous vehicles may also be referred to herein as“driverless car,” “self-driving car,” or “robotic car.” An HD map refersto a map storing data with very high precision, typically 5-10 cm.Embodiments generate HD maps containing spatial geometric informationabout the roads on which an autonomous vehicle can travel. Accordingly,the generated HD maps include the information necessary for anautonomous vehicle navigating safely without human intervention. Insteadof collecting data for the HD maps using an expensive and time consumingmapping fleet process including vehicles outfitted with high resolutionsensors, embodiments of the invention use data from the lower resolutionsensors of the self-driving vehicles themselves as they drive aroundthrough their environments. The vehicles may have no prior map data forthese routes or even for the region. Embodiments of the inventionprovide location as a service (LaaS) such that autonomous vehicles ofdifferent manufacturers can each have access to the most up-to-date mapinformation created via these embodiments of invention.

Embodiments of the invention perform lidar-to-camera calibration for usein generating and maintaining high definition (HD) maps that areaccurate and include the most updated road conditions for safenavigation. For example, the HD maps provide the current location of theautonomous vehicle relative to the lanes of the road precisely enough toallow the autonomous vehicle to drive safely in the lane.

HD maps store a very large amount of information, and therefore facechallenges in managing the information. For example, an HD map for alarge geographic region may not fit on the local storage of a vehicle.Embodiments of the invention provide the necessary portion of an HD mapto an autonomous vehicle that allows the vehicle to determine itscurrent location in the HD map, determine the features on the roadrelative to the vehicle's position, determine if it is safe to move thevehicle based on physical constraints and legal constraints, etc.Examples of physical constraints include physical obstacles, such aswalls, and examples of legal constraints include legally alloweddirection of travel for a lane, speed limits, yields, stops.

Embodiments of the invention allow safe navigation for an autonomousvehicle by providing high latency, for example, 10-20 milliseconds orless for providing a response to a request; high accuracy in terms oflocation, i.e., accuracy within 10 cm or less; freshness of data byensuring that the map is updated to reflect changes on the road within areasonable time frame; and storage efficiency by minimizing the storageneeded for the HD Map.

FIG. 1 shows the overall system environment of an HD map systeminteracting with multiple vehicles, according to an embodiment. The HDmap system 100 includes an online HD map system 110 that interacts witha plurality of vehicles 150. The vehicles 150 may be autonomous vehiclesbut are not required to be. The online HD map system 110 receives sensordata captured by sensors of the vehicles, and combines the data receivedfrom the vehicles 150 to generate and maintain HD maps. The online HDmap system 110 sends HD map data to the vehicles for use in driving thevehicles. In an embodiment, the online HD map system 110 is implementedas a distributed computing system, for example, a cloud based servicethat allows clients such as vehicle computing systems 120 to makerequests for information and services. For example, a vehicle computingsystem 120 may make a request for HD map data for driving along a routeand the online HD map system 110 provides the requested HD map data.

FIG. 1 and the other figures use like reference numerals to identifylike elements. A letter after a reference numeral, such as “105A,”indicates that the text refers specifically to the element having thatparticular reference numeral. A reference numeral in the text without afollowing letter, such as “105,” refers to any or all of the elements inthe figures bearing that reference numeral (e.g. “105” in the textrefers to reference numerals “105A” and/or “105N” in the figures).

The online HD map system 110 comprises a vehicle interface module 160and an HD map store 165. The online HD map system 110 interacts with thevehicle computing system 120 of various vehicles 150 using the vehicleinterface module 160. The online HD map system 110 stores mapinformation for various geographical regions in the HD map store 165.The online HD map system 110 may include other modules than those shownin FIG. 1, for example, various other modules as illustrated in FIG. 4and further described herein.

The online HD map system 110 receives 115 data collected by sensors of aplurality of vehicles 150, for example, hundreds or thousands of cars.The vehicles provide sensor data captured while driving along variousroutes and send it to the online HD map system 110. The online HD mapsystem 110 uses the data received from the vehicles 150 to create andupdate HD maps describing the regions in which the vehicles 150 aredriving. The online HD map system 110 builds high definition maps basedon the collective information received from the vehicles 150 and storesthe HD map information in the HD map store 165.

The online HD map system 110 sends 125 HD maps to individual vehicles150 as required by the vehicles 150. For example, if an autonomousvehicle needs to drive along a route, the vehicle computing system 120of the autonomous vehicle provides information describing the routebeing traveled to the online HD map system 110. In response, the onlineHD map system 110 provides the required HD maps for driving along theroute.

In an embodiment, the online HD map system 110 sends portions of the HDmap data to the vehicles in a compressed format so that the datatransmitted consumes less bandwidth. The online HD map system 110receives from various vehicles, information describing the data that isstored at the local HD map store 275 of the vehicle. If the online HDmap system 110 determines that the vehicle does not have certain portionof the HD map stored locally in the local HD map store 275, the onlineHD map system 110 sends that portion of the HD map to the vehicle. Ifthe online HD map system 110 determines that the vehicle did previouslyreceive that particular portion of the HD map but the corresponding datawas updated by the online HD map system 110 since the vehicle lastreceived the data, the online HD map system 110 sends an update for thatportion of the HD map stored at the vehicle. This allows the online HDmap system 110 to minimize the amount of data that is communicated withthe vehicle and also to keep the HD map data stored locally in thevehicle updated on a regular basis.

A vehicle 150 includes vehicle sensors 105, vehicle controls 130, and avehicle computing system 120. The vehicle sensors 105 allow the vehicle150 to detect the surroundings of the vehicle as well as informationdescribing the current state of the vehicle, for example, informationdescribing the location and motion parameters of the vehicle. Thevehicle sensors 105 comprise a camera, a light detection and rangingsensor (LIDAR), a global positioning system (GPS) navigation system, aninertial measurement unit (IMU), and others. The vehicle has one or morecameras that capture images of the surroundings of the vehicle. A LIDARsurveys the surroundings of the vehicle by measuring distance to atarget by illuminating that target with a laser light pulses, andmeasuring the reflected pulses. The GPS navigation system determines theposition of the vehicle based on signals from satellites. An IMU is anelectronic device that measures and reports motion data of the vehiclesuch as velocity, acceleration, direction of movement, speed, angularrate, and so on using a combination of accelerometers and gyroscopes orother measuring instruments.

The vehicle controls 130 control the physical movement of the vehicle,for example, acceleration, direction change, starting, stopping, and soon. The vehicle controls 130 include the machinery for controlling theaccelerator, brakes, steering wheel, and so on. The vehicle computingsystem 120 continuously provides control signals to the vehicle controls130, thereby causing an autonomous vehicle to drive along a selectedroute.

The vehicle computing system 120 performs various tasks includingprocessing data collected by the sensors as well as map data receivedfrom the online HD map system 110. The vehicle computing system 120 alsoprocesses data for sending to the online HD map system 110. Details ofthe vehicle computing system are illustrated in FIG. 2 and furtherdescribed in connection with FIG. 2.

The interactions between the vehicle computing systems 120 and theonline HD map system 110 are typically performed via a network, forexample, via the Internet. The network enables communications betweenthe vehicle computing systems 120 and the online HD map system 110. Inone embodiment, the network uses standard communications technologiesand/or protocols. The data exchanged over the network can be representedusing technologies and/or formats including the hypertext markuplanguage (HTML), the extensible markup language (XML), etc. In addition,all or some of links can be encrypted using conventional encryptiontechnologies such as secure sockets layer (SSL), transport layersecurity (TLS), virtual private networks (VPNs), Internet Protocolsecurity (IPsec), etc. In another embodiment, the entities can usecustom and/or dedicated data communications technologies instead of, orin addition to, the ones described above.

FIG. 2 shows the system architecture of a vehicle computing system,according to an embodiment. The vehicle computing system 120 comprises aperception module 210, prediction module 215, planning module 220, acontrol module 225, a local HD map store 275, an HD map system interface280, an HD map application programming interface (API) 205, and acalibration module 290. The various modules of the vehicle computingsystem 120 process various type of data including sensor data 230, abehavior model 235, routes 240, and physical constraints 245. In otherembodiments, the vehicle computing system 120 may have more or fewermodules. Functionality described as being implemented by a particularmodule may be implemented by other modules. Some of the modules mayexecute in the online HD map system 110. For example, the calibrationmodule 290 may execute in the online HD map system 110.

The perception module 210 receives sensor data 230 from the sensors 105of the vehicle 150. This includes data collected by cameras of the car,LIDAR, IMU, GPS navigation system, and so on. The perception module 210uses the sensor data to determine what objects are around the vehicle,the details of the road on which the vehicle is travelling, and so on.The perception module 210 processes the sensor data 230 to populate datastructures storing the sensor data and provides the information to theprediction module 215.

The prediction module 215 interprets the data provided by the perceptionmodule using behavior models of the objects perceived to determinewhether an object is moving or likely to move. For example, theprediction module 215 may determine that objects representing road signsare not likely to move, whereas objects identified as vehicles, people,and so on, are either moving or likely to move. The prediction module215 uses the behavior models 235 of various types of objects todetermine whether they are likely to move. The prediction module 215provides the predictions of various objects to the planning module 200to plan the subsequent actions that the vehicle needs to take next.

The planning module 200 receives the information describing thesurroundings of the vehicle from the prediction module 215, the route240 that determines the destination of the vehicle, and the path thatthe vehicle should take to get to the destination. The planning module200 uses the information from the prediction module 215 and the route240 to plan a sequence of actions that the vehicle needs to take withina short time interval, for example, within the next few seconds. In anembodiment, the planning module 200 specifies the sequence of actions asone or more points representing nearby locations that the vehicle needsto drive through next. The planning module 200 provides the details ofthe plan comprising the sequence of actions to be taken by the vehicleto the control module 225. The plan may determine the subsequent actionof the vehicle, for example, whether the vehicle performs a lane change,a turn, acceleration by increasing the speed or slowing down, and so on.

The control module 225 determines the control signals for sending to thecontrols 130 of the vehicle based on the plan received from the planningmodule 200. For example, if the vehicle is currently at point A and theplan specifies that the vehicle should next go to a nearby point B, thecontrol module 225 determines the control signals for the controls 130that would cause the vehicle to go from point A to point B in a safe andsmooth way, for example, without taking any sharp turns or a zig zagpath from point A to point B. The path taken by the vehicle to go frompoint A to point B may depend on the current speed and direction of thevehicle as well as the location of point B with respect to point A. Forexample, if the current speed of the vehicle is high, the vehicle maytake a wider turn compared to a vehicle driving slowly.

The control module 225 also receives physical constraints 245 as input.These include the physical capabilities of that specific vehicle. Forexample, a car having a particular make and model may be able to safelymake certain types of vehicle movements such as acceleration, and turnsthat another car with a different make and model may not be able to makesafely. The control module 225 incorporates these physical constraintsin determining the control signals. The control module 225 sends thecontrol signals to the vehicle controls 130 that cause the vehicle toexecute the specified sequence of actions causing the vehicle to move asplanned. The above steps are constantly repeated every few secondscausing the vehicle to drive safely along the route that was planned forthe vehicle.

The various modules of the vehicle computing system 120 including theperception module 210, prediction module 215, and planning module 220receive map information to perform their respective computation. Thevehicle 150 stores the HD map data in the local HD map store 275. Themodules of the vehicle computing system 120 interact with the map datausing the HD map API 205 that provides a set of application programminginterfaces (APIs) that can be invoked by a module for accessing the mapinformation. The HD map system interface 280 allows the vehiclecomputing system 120 to interact with the online HD map system 110 via anetwork (not shown in the Figures). The local HD map store 275 storesmap data in a format specified by the HD Map system 110. The HD map API205 is capable of processing the map data format as provided by the HDMap system 110. The HD Map API 205 provides the vehicle computing system120 with an interface for interacting with the HD map data. The HD mapAPI 205 includes several APIs including the localization API 250, thelandmark map API 255, the route API 265, the 3D map API 270, the mapupdate API 285, and so on.

The localization APIs 250 determine the current location of the vehicle,for example, when the vehicle starts and as the vehicle moves along aroute. The localization APIs 250 include a localize API that determinesan accurate location of the vehicle within the HD Map. The vehiclecomputing system 120 can use the location as an accurate relativepositioning for making other queries, for example, feature queries,navigable space queries, and occupancy map queries further describedherein. The localize API receives inputs comprising one or more of,location provided by GPS, vehicle motion data provided by IMU, LIDARscanner data, and camera images. The localize API returns an accuratelocation of the vehicle as latitude and longitude coordinates. Thecoordinates returned by the localize API are more accurate compared tothe GPS coordinates used as input, for example, the output of thelocalize API may have precision range from 5-10 cm. In one embodiment,the vehicle computing system 120 invokes the localize API to determinelocation of the vehicle periodically based on the LIDAR using scannerdata, for example, at a frequency of 10 Hz. The vehicle computing system120 may invoke the localize API to determine the vehicle location at ahigher rate (e.g., 60 Hz) if GPS/IMU data is available at that rate. Thevehicle computing system 120 stores as internal state, location historyrecords to improve accuracy of subsequent localize calls. The locationhistory record stores history of location from the point-in-time, whenthe car was turned off/stopped. The localization APIs 250 include alocalize-route API generates an accurate route specifying lanes based onthe HD map. The localize-route API takes as input a route from a sourceto destination via a third party maps and generates a high precisionroutes represented as a connected graph of navigable lanes along theinput routes based on HD maps.

The landmark map API 255 provides the geometric and semantic descriptionof the world around the vehicle, for example, description of variousportions of lanes that the vehicle is currently travelling on. Thelandmark map APIs 255 comprise APIs that allow queries based on landmarkmaps, for example, fetch-lanes API and fetch-features API. Thefetch-lanes API provide lane information relative to the vehicle and thefetch-features API. The fetch-lanes API receives as input a location,for example, the location of the vehicle specified using latitude andlongitude of the vehicle and returns lane information relative to theinput location. The fetch-lanes API may specify a distance parametersindicating the distance relative to the input location for which thelane information is retrieved. The fetch-features API receivesinformation identifying one or more lane elements and returns landmarkfeatures relative to the specified lane elements. The landmark featuresinclude, for each landmark, a spatial description that is specific tothe type of landmark.

The 3D map API 265 provides efficient access to the spatial3-dimensional (3D) representation of the road and various physicalobjects around the road as stored in the local HD map store 275. The 3Dmap APIs 365 include a fetch-navigable-surfaces API and afetch-occupancy-grid API. The fetch-navigable-surfaces API receives asinput, identifiers for one or more lane elements and returns navigableboundaries for the specified lane elements. The fetch-occupancy-grid APIreceives a location as input, for example, a latitude and longitude ofthe vehicle, and returns information describing occupancy for thesurface of the road and all objects available in the HD map near thelocation. The information describing occupancy includes a hierarchicalvolumetric grid of all positions considered occupied in the map. Theoccupancy grid includes information at a high resolution near thenavigable areas, for example, at curbs and bumps, and relatively lowresolution in less significant areas, for example, trees and wallsbeyond a curb. The fetch-occupancy-grid API is useful for detectingobstacles and for changing direction if necessary.

The 3D map APIs also include map update APIs, for example,download-map-updates API and upload-map-updates API. Thedownload-map-updates API receives as input a planned route identifierand downloads map updates for data relevant to all planned routes or fora specific planned route. The upload-map-updates API uploads datacollected by the vehicle computing system 120 to the online HD mapsystem 110. This allows the online HD map system 110 to keep the HD mapdata stored in the online HD map system 110 up to date based on changesin map data observed by sensors of vehicles driving along variousroutes.

The route API 270 returns route information including full route betweena source and destination and portions of route as the vehicle travelsalong the route. The 3D map API 365 allows querying the HD Map. Theroute APIs 270 include add-planned-routes API and get-planned-route API.The add-planned-routes API provides information describing plannedroutes to the online HD map system 110 so that information describingrelevant HD maps can be downloaded by the vehicle computing system 120and kept up to date. The add-planned-routes API receives as input, aroute specified using polylines expressed in terms of latitudes andlongitudes and also a time-to-live (TTL) parameter specifying a timeperiod after which the route data can be deleted. Accordingly, theadd-planned-routes API allows the vehicle to indicate the route thevehicle is planning on taking in the near future as an autonomous trip.The add-planned-route API aligns the route to the HD map, records theroute and its TTL value, and makes sure that the HD map data for theroute stored in the vehicle computing system 120 is up to date. Theget-planned-routes API returns a list of planned routes and providesinformation describing a route identified by a route identifier.

The map update API 285 manages operations related to update of map data,both for the local HD map store 275 and for the HD map store 165 storedin the online HD map system 110. Accordingly, modules in the vehiclecomputing system 120 invoke the map update API 285 for downloading datafrom the online HD map system 110 to the vehicle computing system 120for storing in the local HD map store 275 as necessary. The map updateAPI 285 also allows the vehicle computing system 120 to determinewhether the information monitored by the vehicle sensors 105 indicates adiscrepancy in the map information provided by the online HD map system110 and uploads data to the online HD map system 110 that may result inthe online HD map system 110 updating the map data stored in the HD mapstore 165 that is provided to other vehicles 150.

The calibration module 290 performs various actions related tocalibration of sensors of an autonomous vehicle, for example,lidar-to-camera calibration or lidar-to-lidar calibration. Lidar andcameras of an autonomous vehicle record data in their own coordinatesystems. In an embodiment, the HD map system 100 determines a rigid 3dtransform (a rotation+translation) to convert data from a coordinatesystem to another. In some embodiment, the HD map system 100 usesperspective-n-point techniques for determining a transform from onecoordinate system to another. Tools and modules of HD map system 100that use both data sources require accurate lidar-to-camera calibration,for example, OMap coloring, feature projection, camera-basedlocalization, demo viewer, and so on. In an embodiment, the autonomousvehicle is equipped with one lidar and two cameras (stereo). Thedifferent sensors have a shard field of view. In an embodiment, thecameras have been calibrated individually.

FIG. 4 illustrates the various layers of instructions in the HD Map APIof a vehicle computing system, according to an embodiment. Differentmanufacturer of vehicles have different instructions for receivinginformation from vehicle sensors 105 and for controlling the vehiclecontrols 130. Furthermore, different vendors provide different computeplatforms with autonomous driving capabilities, for example, collectionand analysis of vehicle sensor data. Examples of compute platform forautonomous vehicles include platforms provided vendors, such as NVIDIA,QUALCOMM, and INTEL. These platforms provide functionality for use byautonomous vehicle manufacturers in manufacture of autonomous vehicles.A vehicle manufacturer can use any one or several compute platforms forautonomous vehicles. The online HD map system 110 provides a library forprocessing HD maps based on instructions specific to the manufacturer ofthe vehicle and instructions specific to a vendor specific platform ofthe vehicle. The library provides access to the HD map data and allowsthe vehicle to interact with the online HD map system 110.

As shown in FIG. 3, in an embodiment, the HD map API is implemented as alibrary that includes a vehicle manufacturer adapter 310, a computeplatform adapter 320, and a common HD map API layer 330. The common HDmap API layer comprises generic instructions that can be used across aplurality of vehicle compute platforms and vehicle manufacturers. Thecompute platform adapter 320 include instructions that are specific toeach computer platform. For example, the common HD Map API layer 330 mayinvoke the compute platform adapter 320 to receive data from sensorssupported by a specific compute platform. The vehicle manufactureradapter 310 comprises instructions specific to a vehicle manufacturer.For example, the common HD map API layer 330 may invoke functionalityprovided by the vehicle manufacturer adapter 310 to send specificcontrol instructions to the vehicle controls 130.

The online HD map system 110 stores compute platform adapters 320 for aplurality of compute platforms and vehicle manufacturer adapters 310 fora plurality of vehicle manufacturers. The online HD map system 110determines the particular vehicle manufacturer and the particularcompute platform for a specific autonomous vehicle. The online HD mapsystem 110 selects the vehicle manufacturer adapter 310 for theparticular vehicle manufacturer and the compute platform adapter 320 theparticular compute platform of that specific vehicle. The online HD mapsystem 110 sends instructions of the selected vehicle manufactureradapter 310 and the selected compute platform adapter 320 to the vehiclecomputing system 120 of that specific autonomous vehicle. The vehiclecomputing system 120 of that specific autonomous vehicle installs thereceived vehicle manufacturer adapter 310 and the compute platformadapter 320. The vehicle computing system 120 periodically checks if theonline HD map system 110 has an update to the installed vehiclemanufacturer adapter 310 and the compute platform adapter 320. If a morerecent update is available compared to the version installed on thevehicle, the vehicle computing system 120 requests and receives thelatest update and installs it.

HD Map System Architecture

FIG. 4 shows the system architecture of an HD map system, according toan embodiment. The online HD map system 110 comprises a map creationmodule 410, a map update module 420, a map data encoding module 430, aload balancing module 440, a map accuracy management module, a vehicleinterface module, and a HD map store 165. Other embodiments of online HDmap system 110 may include more or fewer modules than shown in FIG. 4.Functionality indicated as being performed by a particular module may beimplemented by other modules. In an embodiment, the online HD map system110 may be a distributed system comprising a plurality of processors.

The map creation module 410 creates the map from map data collected fromseveral vehicles that are driving along various routes. The map updatemodule 420 updates previously computed map data by receiving more recentinformation from vehicles that recently traveled along routes on whichmap information changed. For example, if certain road signs have changedor lane information has changed as a result of construction in a region,the map update module 420 updates the maps accordingly. The map dataencoding module 430 encodes map data to be able to store the dataefficiently as well as send the required map data to vehicles 150efficiently. The load balancing module 440 balances load across vehiclesto ensure that requests to receive data from vehicles are uniformlydistributed across different vehicles. The map accuracy managementmodule 450 maintains high accuracy of the map data using varioustechniques even though the information received from individual vehiclesmay not have high accuracy.

FIG. 5 illustrates the components of an HD map, according to anembodiment. The HD map comprises maps of several geographical regions.The HD map 510 of a geographical region comprises a landmark map (LMap)520 and an occupancy map (OMap) 530. The landmark map comprisesinformation describing lanes including spatial location of lanes andsemantic information about each lane. The spatial location of a lanecomprises the geometric location in latitude, longitude and elevation athigh prevision, for example, at or below 10 cm precision. The semanticinformation of a lane comprises restrictions such as direction, speed,type of lane (for example, a lane for going straight, a left turn lane,a right turn lane, an exit lane, and the like), restriction on crossingto the left, connectivity to other lanes and so on. The landmark map mayfurther comprise information describing stop lines, yield lines, spatiallocation of cross walks, safely navigable space, spatial location ofspeed bumps, curb, and road signs comprising spatial location and typeof all signage that is relevant to driving restrictions. Examples ofroad signs described in an HD map include stop signs, traffic lights,speed limits, one-way, do-not-enter, yield (vehicle, pedestrian,animal), and so on.

The occupancy map 530 comprises spatial 3-dimensional (3D)representation of the road and all physical objects around the road. Thedata stored in an occupancy map 530 is also referred to herein asoccupancy grid data. The 3D representation may be associated with aconfidence score indicative of a likelihood of the object existing atthe location. The occupancy map 530 may be represented in a number ofother ways. In one embodiment, the occupancy map 530 is represented as a3D mesh geometry (collection of triangles) which covers the surfaces. Inanother embodiment, the occupancy map 530 is represented as a collectionof 3D points which cover the surfaces. In another embodiment, theoccupancy map 530 is represented using a 3D volumetric grid of cells at5-10 cm resolution. Each cell indicates whether or not a surface existsat that cell, and if the surface exists, a direction along which thesurface is oriented.

The occupancy map 530 may take a large amount of storage space comparedto a landmark map 520. For example, data of 1 GB/Mile may be used by anoccupancy map 530, resulting in the map of the United States (including4 million miles of road) occupying 4×10¹⁵ bytes or 4 petabytes.Therefore the online HD map system 110 and the vehicle computing system120 use data compression techniques for being able to store and transfermap data thereby reducing storage and transmission costs. Accordingly,the techniques disclosed herein make self-driving of autonomous vehiclespossible.

In one embodiment, the HD Map does not require or rely on data typicallyincluded in maps, such as addresses, road names, ability to geo-code anaddress, and ability to compute routes between place names or addresses.The vehicle computing system 120 or the online HD map system 110accesses other map systems, for example, GOOGLE MAPs to obtain thisinformation. Accordingly, a vehicle computing system 120 or the onlineHD map system 110 receives navigation instructions from a tool such asGOOGLE MAPs into a route and converts the information to a route basedon the HD map information.

Geographical Regions in HD Maps

The online HD map system 110 divides a large physical area intogeographical regions and stores a representation of each geographicalregion. Each geographical region represents a contiguous area bounded bya geometric shape, for example, a rectangle or square. In an embodiment,the online HD map system 110 divides a physical area into geographicalregions of the same size independent of the amount of data required tostore the representation of each geographical region. In anotherembodiment, the online HD map system 110 divides a physical area intogeographical regions of different sizes, where the size of eachgeographical region is determined based on the amount of informationneeded for representing the geographical region. For example, ageographical region representing a densely populated area with a largenumber of streets represents a smaller physical area compared to ageographical region representing sparsely populated area with very fewstreets. Accordingly, in this embodiment, the online HD map system 110determines the size of a geographical region based on an estimate of anamount of information required to store the various elements of thephysical area relevant for an HD map.

In an embodiment, the online HD map system 110 represents a geographicregion using an object or a data record that comprises variousattributes including, a unique identifier for the geographical region, aunique name for the geographical region, description of the boundary ofthe geographical region, for example, using a bounding box of latitudeand longitude coordinates, and a collection of landmark features andoccupancy grid data.

FIGS. 6A-B illustrate geographical regions defined in an HD map,according to an embodiment. FIG. 6A shows a square geographical region610 a. FIG. 6B shows two neighboring geographical regions 610 a and 610b. The online HD map system 110 stores data in a representation of ageographical region that allows for smooth transition from onegeographical region to another as a vehicle drives across geographicalregion boundaries.

According to an embodiment, as illustrated in FIG. 6, each geographicregion has a buffer of a predetermined width around it. The buffercomprises redundant map data around all 4 sides of a geographic region(in the case that the geographic region is bounded by a rectangle). FIG.6A shows a boundary 620 for a buffer of 50 meters around the geographicregion 610 a and a boundary 630 for buffer of 100 meters around thegeographic region 610 a. The vehicle computing system 120 switches thecurrent geographical region of a vehicle from one geographical region tothe neighboring geographical region when the vehicle crosses a thresholddistance within this buffer. For example, as shown in FIG. 6B, a vehiclestarts at location 650 a in the geographical region 610 a. The vehicletraverses along a route to reach a location 650 b where it cross theboundary of the geographical region 610 but stays within the boundary620 of the buffer. Accordingly, the vehicle computing system 120continues to use the geographical region 610 a as the currentgeographical region of the vehicle. Once the vehicle crosses theboundary 620 of the buffer at location 650 c, the vehicle computingsystem 120 switches the current geographical region of the vehicle togeographical region 610 b from 610 a. The use of a buffer prevents rapidswitching of the current geographical region of a vehicle as a result ofthe vehicle travelling along a route that closely tracks a boundary of ageographical region.

Lane Representations in HD Maps

The HD map system 100 represents lane information of streets in HD maps.Although the embodiments described herein refer to streets, thetechniques are applicable to highways, alleys, avenues, boulevards, orany other path on which vehicles can travel. The HD map system 100 useslanes as a reference frame for purposes of routing and for localizationof a vehicle. The lanes represented by the HD map system 100 includelanes that are explicitly marked, for example, white and yellow stripedlanes, lanes that are implicit, for example, on a country road with nolines or curbs but two directions of travel, and implicit paths that actas lanes, for example, the path that a turning car makes when entering alane from another lane. The HD map system 100 also stores informationrelative to lanes, for example, landmark features such as road signs andtraffic lights relative to the lanes, occupancy grids relative to thelanes for obstacle detection, and navigable spaces relative to the lanesso the vehicle can efficiently plan/react in emergencies when thevehicle must make an unplanned move out of the lane. Accordingly, the HDmap system 100 stores a representation of a network of lanes to allow avehicle to plan a legal path between a source and a destination and toadd a frame of reference for real time sensing and control of thevehicle. The HD map system 100 stores information and provides APIs thatallow a vehicle to determine the lane that the vehicle is currently in,the precise vehicle location relative to the lane geometry, and allrelevant features/data relative to the lane and adjoining and connectedlanes.

FIG. 7 illustrates lane representations in an HD map, according to anembodiment. FIG. 7 shows a vehicle 710 at a traffic intersection. The HDmap system provides the vehicle with access to the map data that isrelevant for autonomous driving of the vehicle. This includes, forexample, features 720 a and 720 b that are associated with the lane butmay not be the closest features to the vehicle. Therefore, the HD mapsystem 100 stores a lane-centric representation of data that representsthe relationship of the lane to the feature so that the vehicle canefficiently extract the features given a lane.

The HD map system 100 represents portions of the lanes as lane elements.A lane element specifies the boundaries of the lane and variousconstraints including the legal direction in which a vehicle can travelwithin the lane element, the speed with which the vehicle can drivewithin the lane element, whether the lane element is for left turn only,or right turn only, and so on. The HD map system 100 represents a laneelement as a continuous geometric portion of a single vehicle lane. TheHD map system 100 stores objects or data structures representing laneelements that comprise information representing geometric boundaries ofthe lanes; driving direction along the lane; vehicle restriction fordriving in the lane, for example, speed limit, relationships withconnecting lanes including incoming and outgoing lanes; a terminationrestriction, for example, whether the lane ends at a stop line, a yieldsign, or a speed bump; and relationships with road features that arerelevant for autonomous driving, for example, traffic light locations,road sign locations and so on.

Examples of lane elements represented by the HD map system 100 include,a piece of a right lane on a freeway, a piece of a lane on a road, aleft turn lane, the turn from a left turn lane into another lane, amerge lane from an on-ramp an exit lane on an off-ramp, and a driveway.The HD map system 100 represents a one lane road using two laneelements, one for each direction. The HD map system 100 representsmedian turn lanes that are shared similar to a one-lane road.

FIGS. 8A-B illustrates lane elements and relations between lane elementsin an HD map, according to an embodiment. FIG. 8A shows an example of aT junction in a road illustrating a lane element 810 a that is connectedto lane element 810 c via a turn lane 810 b and is connected to lane 810e via a turn lane 810 d. FIG. 8B shows an example of a Y junction in aroad showing label 810 f connected to lane 810 h directly and connectedto lane 810 i via lane 810 g. The HD map system 100 determines a routefrom a source location to a destination location as a sequence ofconnected lane elements that can be traversed to reach from the sourcelocation to the destination location.

Lidar-to-Camera Calibration

FIG. 9 illustrates the system architecture of a sensor calibrationmodule, according to an embodiment. The sensor calibration modulecomprises various modules including pattern based calibration module910, still frame detection module 920, a checkerboard pattern placementmodule 930, edgel based calibration module 950, and transform store 940.Other embodiments may include more of fewer modules. The modulesdescribed herein may be stored and executed in the vehicle computingsystem, in the online HD map system, or both. Steps described as beingperformed by a particular module may be performed by other modules thanthose indicated herein. The pattern based calibration module performscalibration based on a pattern, for example, checkerboard pattern thatis captured by sensors of the vehicle. The still frame detection module920 detects still frames from a video for use in calibration. The edgelbased calibration module 950 performs edgel based calibration asdescribed in FIGS. 21, 22, and 23. The transform store 940 stores valuesof various transforms that are determined by the HD map system. Thetransforms are used by other modules, for example, for HD mapgeneration. The checkerboard pattern placement module 930 helps withplacement of checkerboard pattern, for example, by executing the processillustrated in FIG. 20.

According to some embodiments, the HD map system receives sensor data ofscenes including a checkerboard pattern and uses the sensor data forperforming calibration. The checkerboard pattern may be placed atvarious locations in front of the vehicle by a user. The vehicle maycapture a video comprising images including the checkerboard pattern.The HD map system extracts sensor data from frames of the video andanalyzes the sensor data to perform calibration.

FIG. 10(A) illustrates sensor data obtained from a scene comprising acheckerboard pattern 1010 held in front of a vehicle, according to anembodiment. The checkerboard pattern is kept in front of the vehiclesensors including the lidar and camera. A lidar scan of the sceneshowing the checkerboard pattern is captured by the vehicle lidar andimages of the s of the checkerboard pattern are captured using thevehicle cameras. The lidar scans and camera images are used forcalibrating the vehicle sensors.

The pattern used for calibration is not limited to a checkerboardpattern and can be other types of patterns, for example, alternatingstripes. FIG. 10(B) illustrates sensor data obtained from a scenecomprising a pattern 1020 including different colored tapes, forexample, alternating red and blue tapes, according to an embodiment. TheHD map system analyzes the sensor data to detect edges in the patternand uses the information for calibration of sensors.

A user places the pattern at various distances and locations so as tocover different areas visible from sensors of the vehicle. In anembodiment, the HD map system captures the sensor data including thesepatterns and determines a set of 3d-to-2d correspondences between lidarpoints and image pixels. The HD map system converts the information ofthe 3d-to-2d correspondences to a perspective-n-point (PnP) problem andsolves the problem, for example, using Levenberg-Marquardt technique.The HD map system detects 2d checkerboard corners from camera images,with subpixel accuracy.

The perspective-n-point (PnP) problem is the problem of estimating thepose of a calibrated camera given a set of N 3D points in the world andtheir corresponding 2D projections in the image. The camera pose isrepresented using 6 degrees-of-freedom (DOF) comprising the rotation(roll, pitch, and yaw) and 3D translation of the camera with respect tothe world. For example, techniques for solving the perspective-n-pointproblem for N=3 are called P3P, and other techniques are used forsolving the perspective-n-point problem for N>3. Accordingly, techniquesfor solving the perspective-n-point problem are referred to herein asperspective-n-point techniques.

A perspective-n-point technique receives input comprising a set of N 3Dpoints in a reference frame and their corresponding 2D image projectionsas well as the calibrated intrinsic camera parameters, and determinesthe 6 DOF pose of the camera in the form of its rotation and translationwith respect to the world. Given a pose of the camera, theperspective-n-point technique can be used to determine the calibratedintrinsic camera parameters and therefore used for performingcalibration of the camera. The parameters of the camera that arecalibrated include intrinsic properties of the camera such as the focallength, principal image point, skew parameter, and other parameters. Ifthe perspective-n-point technique determines multiple solutions, the HDmap system selects a particular solution by performing post-processingof the solution set. The HD map system may use RANSAC with a PnPtechnique to make the solution robust to outliers in the set of pointcorrespondences.

The HD map system detects corners of the pattern from lidar points.Detecting corners from lidar points is challenging for various reasons.Lidar points are a lot sparser compared to image pixels. Typically lidarpoints are 0.2 degree apart on the same scan line, and greater than onedegree apart across scan lines. Furthermore, lidar points are noisy inboth range and intensity values. Range values have a 1-sigma error of 2cm and the checkerboard point cloud has a 5 cm thickness. Intensityvalues have a large variation across scan lines. There can be ghostpoints near physical boundaries. There can be missing points nearintensity boundaries. All these issues with lidar points make itdifficult to detect 3d corners from lidar points. Techniques disclosedherein determine the corners of the pattern with high accuracy.

The HD map system may have multiple vehicles running on a daily basisfor data collection or demo purposes, and there may be a large fleet.Calibration parameters drift over time. Therefore, every car needs to bere-calibrated periodically. Manual calibration that involves an expertcan be expensive since the vehicle must be brought to a facilityoperated by experts. Embodiments provide a predefined calibrationprocedure that guarantees successful calibration without intervention bya human expert. The procedure uses objects that are portable so thatremote users that are not experts can calibrate their cars. The proposedembodiments require a checkerboard and a fairly simple procedure whichcan be automated.

When the checkerboard is close, for example, within 4 meters, thecheckerboard points form a dominant plane within a small radius aroundthe lidar, because there are very few other objects within this radius.The sensor calibration module 290 determines this dominant plane. Whenthe checkerboard is farther away, however, the environment can be fullof other planar objects, for example, walls, cabinets, the side of othercars. The checkerboard is typically smaller compared to these objects.As a result, extracting the not-so-big checkerboard is difficult withoutany prior knowledge of where it is.

Overall Process of Lidar-to-Camera Calibration

Although the processes described herein use a checkerboard pattern forillustrative purposes, the embodiments are not limited to use ofcheckerboard pattern and can be used with other patterns, for example,striped pattern. Also the processes are described in the context ofautonomous vehicles but are not limited to autonomous vehicle and can beapplied to other vehicles that may not be autonomous, robots, or anyother device that mounts multiple sensors that can drift over time.

FIG. 11 shows a flowchart illustrating the overall process oflidar-to-camera calibration according to an embodiment. The sensorcalibration module 290 extracts and refines checkerboard corners usingpoints on the board. The sensor calibration module 290 uses robustnessestimators (RANSAC) where possible to minimize the impact of noise.

The sensor calibration module 290 determines 1110 an approximatelidar-to-camera transform using lidar frames of a pattern that is closeto the vehicle sensors. This step represents the first pass of theprocess. For example, the checkerboard pattern is held in front ofsensors of the vehicle within a threshold distance. As a result, atleast more than a threshold amount of scene captured by the sensorscomprises the checkerboard pattern.

The sensor calibration module 290 uses the approximate lidar-to-cameratransform to determine 1120 an accurate lidar-to-camera transform usingimages of checkerboard located at a distance. This step represents thesecond pass of the process. Accordingly, the checkerboard pattern isheld more than a threshold distance from the sensors of the vehicle suchthat there can be multiple other objects in the scene besides thecheckerboard.

Subsequently, the HD map system receives 1130 sensor data from sensorsof the vehicle including the camera sensor and lidar sensor, forexample, data captured as the vehicle drives along various routes. TheHD map system generates 1140 HD maps using the received sensor data andthe lidar-to-camera transforms determined by calibrating the sensors ofthe vehicle. For example, the lidar-to-camera transform is used forcorrelating the data captured by lidar and camera sensors and combiningthe data to obtain a consistent view of the surroundings of the vehicle.The vehicle uses 1150 the HD map for various purposes including guidingthe vehicle, displaying map data and other applications related todriving of the vehicle or self-driving.

Following are the details of step 1110 for determining the approximatelidar-to-camera transform based on close-up views of the checkerboardpattern. FIG. 12 shows a flowchart illustrating the process of the firstphase of lidar-to-camera calibration based on a close view of thecheckerboard, according to an embodiment. The sensors of the autonomousvehicles obtain a video with the checkerboard located within the fieldof view of the sensors at a close distance, for example, within a fewmeters of the autonomous vehicle. In the first pass, the sensorcalibration module 290 processes the frames in which the checkerboard isclose using a simple plane fitting method. The sensor calibration module290 determines whether the checkerboard is close. If the sensorcalibration module 290 fails to locate the checkerboard in a frame, thesensor calibration module 290 skips the frame and processes the nextframe.

As shown in the flowchart illustrated in FIG. 12, the sensor calibrationmodule 290 selects 1210 a frame from the captured video. The sensorcalibration module 290 reads 1220 a set of lidar points from theselected frame. The sensor calibration module 290 selects a subset oflidar points that are close to the sensor. For example, the sensorcalibration module 290 selects 1230 a subset of the lidar points thathave a range less than a threshold distance, for example, less than 4meters and yaw less than a threshold angle, for example, less than 60degrees of camera facing direction. The sensor calibration module 290fits 1240 a dominant plane within the selected points, for example,using techniques such as random sample consensus (RANSAC). The sensorcalibration module 290 uses 1250 the selected frame if number of inliersis greater than a threshold value, otherwise the sensor calibrationmodule 290 skips the frame and repeats the above steps by selecting 1210another frame. The sensor calibration module 290 uses the selected frameto determine corners of the checkerboard pattern.

After the first pass, the sensor calibration module 290 has determinedall the 3d points representing corners of the checkerboard pattern nearthe lidar and determines a rough lidar-to-camera transform by solvingthe PnP problem. In the second pass, the sensor calibration module 290processes all the frames again, but this time uses sensor datacomprising the checkerboard pattern at various distances including adistances greater than a threshold value. The sensor calibration module290 triangulates the 2d checkerboard corners detected from left andright camera views, and uses the rough lidar-to-camera transformcomputed during the first pass to estimate where the corners are inlidar coordinates. The sensor calibration module 290 only keeps lidarpoints within a small radius of the estimated location. In anembodiment, the sensor calibration module 290 uses a value of the radiusthat is slightly larger than the half length of the checkerboard. Thesensor calibration module 290 ensures that among the remaining points, amajority of them should be on the checkerboard. The sensor calibrationmodule 290 again resorts to a plane fitting method to fit a planethrough the points determined to represent the checkerboard. The stepsof the process corresponding to the second phase are as follows.

FIG. 13 shows a flowchart illustrating the process of the second phaseof lidar-to-camera calibration that determines an accuratelidar-to-camera transform based on a distant view of the checkerboard,according to an embodiment. The sensors of the autonomous vehiclescapture a video with the checkerboard positioned at more than athreshold distance while in the field of view of the sensors. In anembodiment, the sensors of the autonomous camera include a left camera,a right camera, and a lidar.

The sensor calibration module 290 selects 1310 a frame from the video.The sensor calibration module 290 detects 1320 2D points representingcheckerboard corners from left and right camera images. The sensorcalibration module 290 triangulates 1330 corresponding 2D points to findtheir 3D location in camera coordinates. The sensor calibration module290 applies 1340 approximate lidar-to-camera transform to convert 3Dpoints to lidar coordinates. The sensor calibration module 290 reads1350 lidar points and selects a subset within a threshold radius of anestimated checkerboard center. The sensor calibration module 290 fits1360 dominant plane within the selected points using RANSAC. The sensorcalibration module 290 uses 1370 the selected frame if number of inliersis greater than a threshold value, for example 100 inliers. Otherwisethe sensor calibration module 290 selects 1310 another frame and repeatsthe above steps.

The process illustrated in FIG. 13 is based on use of two cameras of theautonomous vehicle. FIG. 14 shows a flowchart illustrating a process fordetecting the checkerboard pattern based on a use of a single camera,according to an embodiment. Instead of using stereo triangulation, theembodiment uses lidar-assisted plane fitting. Accordingly, the HD mapsystem performs the calibration even if the checkerboard pattern isdetected in the view of only one of the cameras. The sensor calibrationmodule 290 selects a frame and analyzes the frame to detect 1410 thecheckerboard pattern in at least one of the left camera image or theright camera image. If the sensor calibration module 290 detects thecheckerboard pattern in both views, i.e., left camera view and rightcamera view, the sensor calibration module 290 selects any one view, forexample, the left view. If the sensor calibration module 290 fails todetect the checkerboard pattern in either view, the sensor calibrationmodule 290 skips that particular frame and selects another frame of thevideo. With the checkerboard pattern detected in one view, the sensorcalibration module 290 determines 1420 the bounding polygon of thecheckerboard pattern by identifying its four outer corners in the frame.Then the sensor calibration module 290 projects 1430 lidar points ontothe image using an approximate lidar-to-camera transform, for example,the lidar-to-camera transform determined by the process illustrated inFIG. 13. The sensor calibration module 290 selects 1440 the lidar pointsprojected onto the image that fall inside the polygon area. The sensorcalibration module 290 fits 1450 a plane using the collected points in3d. The sensor calibration module 290 determines 1460 the checkerboardplane geometry in 3d and also determines the 3d coordinates of all thecheckerboard corners. The sensor calibration module 290 classifies 1470all the lidar points, considering the ones within threshold distance tothe plane (for example, a small distance of 10 cm) and within thresholddistance of one of the corners (for example, a small distance of 20 cm)to be on the checkerboard.

Based on the computations performed by the HD map system as illustratedin FIGS. 11, 12, 13, and 14 above, the HD map system obtains a set ofcandidate points on the checkerboard. The HD map system next fits theboundary and normal on the checkerboard. Assuming the checkerboard isheld angled (as required by the calibration procedure), the systemexecutes the steps illustrated in FIG. 15.

FIG. 15 shows a flowchart illustrating the process of fitting boundarypoints and a normal on the checkerboard, according to an embodiment.

The sensor calibration module 290 identifies 1510 points at thecheckerboard boundary as the first and last columns of scan linesegments on the checkerboard. In an embodiment, the sensor calibrationmodule 290 processes two adjacent sides, for example, only the left sideof the boundary including both upper and lower sides for checkerboardfitting purpose. The following discussion describes the method inrelation to processing the upper left and lower left boundaries of thecheckerboard pattern but can be performed for any two adjacent sides,for example, upper left and upper right side, lower left and lower rightsides, and so on. The sensor calibration module 290 identifies ghostpoints near checkerboard boundary, especially on the left side. If alidar is scanning from left to right, the laser goes from far away tonearby points at the left side boundary. The sensor calibration module290 may ignore such a first column. Instead, the sensor calibrationmodule 290 picks the first column that has a neighbor to its right. Thesensor calibration module 290 identifies ghost points as points thatusually occur a little distance (about 5 cm) away from true boundarythat are followed by a gap of 4-5 missing columns. Notice that this maynot always be accurate, as sometimes the true boundary can also fallsomewhere else within the gap. However, the sensor calibration module290 uses this step is to compute a rough geometry of the checkerboard,and refine it using intensity data. Accordingly, the sensor calibrationmodule 290 is able to tolerate an error of a few centimeters.

The sensor calibration module 290 splits 1520 the left boundary intoupper and lower sides by identifying the turning point corresponding tothe left most corner of the checkerboard pattern. These sides correspondto two sides of the checkerboard. The sensor calibration module 290identifies the turning point as having the minimum x (to the left). Thesensor calibration module 290 classifies the points above the turningpoint as the upper side, and points below the turning point as the lowerside. The sensor calibration module 290 may discard the turning pointitself, since it can be considered as belonging to either side.

The sensor calibration module 290 projects 1530 the upper/lower sidepoints to the checkerboard plane. This is so because the boundary pointsare usually noisy in range, as half of the laser beam may hit somebackground object far away, causing its range to be interpolated. Thesensor calibration module 290 projects 1530 the upper/lower side pointsto the checkerboard plane to eliminate such errors.

The sensor calibration module 290 fits 1540 the checkerboard geometryfrom boundary points. In an embodiment, the sensor calibration module290 fits two perpendicular lines that best fit the upper side and lowerside boundary points. The sensor calibration module 290 marks the twolines as the X and Y axes of the checkerboard, and their intersection asthe origin.

In order to handle noise in the data while enforcing the fitted lines tobe always perpendicular, the sensor calibration module 290 uses a RANSACalgorithm, for example, the following 3-point RANSAC algorithm. Duringeach iteration, the sensor calibration module 290 performs the followingsteps: (a.) Randomly select 2 points from the upper side, for example,point A and point B (b.) Randomly select 1 point from the lower side,for example, point C (c.) Fit y axis using points A, B (d.) Projectpoint C to the y axis to obtain point D, and mark the point D as theorigin (e.) Fit x axis using points C and D, and (f.) Count the numberof inliers, i.e., boundary points close to the fitted x and y axes.

The sensor calibration module 290 derives 1550 the location of thecheckerboard pattern using pre-measured offset of the checkerboardpattern on the board including the corners of the checkerboard pattern.

Refining Checkerboard Pattern Using Intensity Data

The checkerboard corners fitted as shown in FIGS. 11-15 may contain asmall amount of error, for example, due to ghost points, missing points,and noise in range values. The sensor calibration module 290 uses theintensity value associated to each lidar point to refine the location ofcheckerboard corners. For each lidar point, its intensity value (forexample, a value in the range of [0, 255]) measures the reflectivity ofthe object, with 0 being black, absorbent diffuse reflector, 100 beingwhite, reflective diffuse reflector, and 255 being completeretro-reflector.

According to the definition of lidar intensity, the black squares on thecheckerboard should produce near-0 intensity values, while the whitesquares on the checkerboard should be close to 100. Given thisinformation, the sensor calibration module 290 performs a full search ina small neighborhood of the parameter space, by varying the location ofthe checkerboard pattern, and measures the alignment of black and whitesquares to underlying intensity data

FIG. 16 shows a flowchart illustrating the process of refining thecheckerboard pattern using intensity data, according to an embodiment.

The sensor calibration module 290 defines 1610 checkerboard coordinates.In an embodiment, the sensor calibration module 290 defines 1610checkerboard coordinates with origin at the top-left corner of thecheckerboard pattern, X-axis pointing down along the short side, Y-axispointing right along the long side, and Z-axis pointing towards thelidar. The sensor calibration module 290 converts 1620 points of thecheckerboard pattern from lidar to checkerboard coordinates.

The sensor calibration module 290 repeats the steps 1630, 1640, 1650,1660, and 1670. The sensor calibration module 290 transforms 1630checkerboard points by small amounts, by varying translation in x, y androtation around z. The sensor calibration module 290 projects 1640 eachcheckerboard point to the checkerboard pattern and determine the color(black or white) of square that the point falls into. The sensorcalibration module 290 determines 1650 the alignment score for eachcheckerboard point based on intensity of square the point falls into.

In an embodiment, the sensor calibration module 290 determines 1650 thealignment score for each checkerboard point as a value matching theintensity if the checkerboard point falls in a white square and as avalue (255—intensity) if the checkerboard point falls in a black square.The sensor calibration module 290 determines 1660 a final score for thistransform as sum of the alignment scores of all checkerboard points. Ifthe sensor calibration module 290 determines that the current transformhas higher score than the previous best transform, the sensorcalibration module 290 uses the current transform as the best transform.Accordingly, the sensor calibration module 290 uses the transform (i.e.,delta of translation in x, y and rotation around z) with highestalignment score as the final transform.

The sensor calibration module 290 applies the inverse of this transformto the checkerboard pattern to convert the checkerboard pattern backfrom checkerboard coordinates to lidar coordinates. The sensorcalibration module 290 uses the converted checkerboard corners as thefinal locations of checkerboard corners.

The steps 1630, 1640, 1650, 1660, and 1670 that are repeated perform afull search for 3 of the 6 degrees of freedom of a rigid transform.Accordingly, the sensor calibration module 290 assumes that the planefitted in the previous iteration is correct, and only allows in-planetransform for refining the checkerboard pattern. This performs betterthan doing a full 6 degrees of freedom search, as (1) the plane fittingstep was already performed using a robustness estimator (RANSAC) andprocesses multiple point samples, thereby reducing the impact of noisein range, so refinement using intensity is unlikely to improve it, and(2) with a lower dimensional search space, the system can search abigger neighborhood with higher computational efficiency.

There can be noise in intensity data as well. There can be a significantvariance in intensity among scan lines. For example, when the lidarfaces a white wall, different intensity values can be observed amongdifferent scan lines, even though the reflectivity of the wall should beuniform. Similarly, when the laser hits an intensity boundary, forexample, from plastic board to reflective tape, there may be a gap of4-5 missing lidar points near the boundary. This may happen since laserscan get saturated by the sudden increase in reflectivity.

In an embodiment, the sensor calibration module 290 uses two additionalconstraints on intensity-based refinement. The sensor calibration module290 skips refinement for checkerboards that are too far away (i.e., morethan a threshold, for example a threshold of 10 meters) from the lidar.This is so because for checkerboard patterns based on very farcheckerboards, too few points may be available on the checkerboard forrobust alignment. Furthermore, sensor calibration module 290 measuresthe maximum movement of any checkerboard corner before and afterrefinement. The sensor calibration module 290 claims failure if itexceeds certain threshold (e.g., 5 cm). This is so, because the sensorcalibration module 290 assumes that the checkerboard fitted fromprevious steps should already be fairly accurate, and if largemodifications need to be made, it is probably caused by noise in theintensity data. Accordingly, the sensor calibration module 290 decidesto skip this frame and try another frame.

Combining Left Camera and Right Camera Points

The standard input to a PnP solver includes a set of 3d-to-2d pointcorrespondences and a 3×3 camera matrix. The sensor calibration module290 has two sets of 2d points extracted from left and right cameras,corresponding to the same set of 3d points extracted from lidar. Sincethe left and right cameras are stereo rectified, their projectionmatrices are in the following form, where P_(left) is the projectionmatrix of the left camera and f_(right) is the projection matrix of theright camera.

P _(left)=[f _(x,)0,c _(x),0;0,f _(y) ,c _(y),0;0,0,1,0]

P _(right)=[f _(x,)0,c _(x) ,t;0,f _(y) ,c _(y),0;0,0,1,0]

The two projection matrices differ in the 4^(th) element where the rightcamera has an offset t=T_(x)·f where T_(x) is the relative translationin camera x coordinate in meters.

If the system can tweak the 3d points in a way that cancels t, it willbe able to use the same 3×3 matrix for both cameras. Given a 3d point inhomogeneous coordinate (x,y,z,1), it projects to the following imagecoordinate in the right camera:

(u,v,w)=(f _(x) ·x+c _(x) ·z+t,f _(y) ·y+c _(y) ·z,z)

In this equation, only u is affected by t. The sensor calibration module290 removes t by transforming x to x′ such that,

f _(x) ·x+c _(x) ·z+t=f _(x) ·x′+c _(x) ·z ⇒x′=x+t/f _(x) =x+T _(x)

Accordingly, the sensor calibration module 290 modifies the x coordinateof each 3d point for the right camera by adding T_(x) to it. This allowssensor calibration module 290 to use identical camera matrices for bothleft and right cameras and thereby optimize the 3d-to-2d projection forboth cameras simultaneously.

Still Frame Selection

The sensor calibration module 290 avoids using frames for calibrationwhere the checkerboard is moving. For almost all lidar devices, allpoints are not captured at the same time. For example, VELODYNE rotatesat 10 Hz and captures points column by column. If the checkerboard movesduring the duration of the scan (0.1 second), the captured point cloudmay not be geometrically consistent, as it captures different parts ofthe checkerboard at different times.

In some embodiments, as part of the calibration procedure, the sensorcalibration module 290 requires the operator to hold the checkerboardstill for at least 3 seconds at each spot. This section describes anautomatic algorithm for selecting these still frames for calibration.

Techniques based on entire point clouds or entire images may not workwell, because even if the system requires the checkerboard to be still,other objects (e.g., people) can move in the environment. The system mayselect not only still frames, but also distinct ones. Furthermore, if acheckerboard stays still for 3 seconds (say a batch of 30 frames), thesystem may only select a single frame out of this batch of frames.

The sensor calibration module 290 initializes 1710 sets H and S as emptylists, where H represents historical checkerboard locations and Srepresents selected frames. The sensor calibration module 290 repeatsthe following steps. The sensor calibration module 290 selects 1720 anew frame and corresponding left and right camera images. The sensorcalibration module 290 detects 1730 checkerboard pattern in both leftand right camera images. The sensor calibration module 290 triangulates1740 corresponding 2d corners from left and right camera images todetermine their 3d locations in camera coordinate. The sensorcalibration module 290 adds 1750 the 3D locations of corners to set Hfor future reference. The sensor calibration module 290 compares the 3Dlocations of corners of the checkerboard to the 3D locations of cornersin set H to determine whether the movement between 3D locations ofcorners compared with 3d locations of corners in the set H at k seconds(for example, 1 second) ago is less than x cm. If the sensor calibrationmodule 290 determines that the movement between 3D locations of cornerscompared with 3d locations of corners in H at k seconds ago is less thanthe threshold distance, the sensor calibration module 290 marks theframe at k seconds ago as a still frame and selects it as a candidatestill frame. The sensor calibration module 290 compares the candidatestill frame to all frames in S. If the sensor calibration module 290determines that the minimum movement between current frame and any framein S is larger than a threshold distance, for example, 20 cm, the sensorcalibration module 290 determines that this candidate frame is likely tobe a distinct still frame and adds the candidate frame to set S. Thesensor calibration module 290 returns the set S as the set of stillframes.

The process shown in FIG. 17 can be executed either online or offline aspost processing. The output of the process shown in FIG. 17 provides alist of frames for use as input calibration data.

The embodiment illustrated in FIG. 17 above relies on triangulation ofcheckerboard corners. Accordingly, the process may not work forsingle-camera setups, and in order for a frame to be considered forselection, both views must see the checkerboard in full. This means theHD map system may not get constraint near image boundary as it is likelyto be partially outside for the other view. Such constraints may berelevant for robust calibration. Furthermore, triangulation errors canbe amplified when the checkerboard is farther away, causing unstableresults. Following embodiments of still frame selection address theseissues.

The sensor calibration module 290 detects checkerboard corners from theimage from a single view. Since the HD map system has the knowledge ofthe dimensions of the checkerboard (grid size and row/column count), thesensor calibration module 290 determines the 3D coordinate of eachcheckerboard corner in checkerboard coordinate. The checkerboardcoordinate is defined to be centered at the upper left corner, withX-axis pointing along the short side, Y-axis pointing along the longside, and Z-axis pointing towards the camera. With the coordinate systemdefined, the sensor calibration module 290 derives the 3d coordinate foreach corner, thereby getting a list of 3d-to-2d correspondences.

The sensor calibration module 290 then determines thecheckerboard-to-camera transform (a rotation and translation) by solvinga PnP problem using, e.g., Levenberg-Marquardt. This works as long asone view shows the checkerboard in full. If the autonomous vehicle doeshave a stereo camera and both views see the checkerboard in full, thesystem can use the method explained above in the “Combining Left/RightCamera Points” section to combine both constraints into one PnP problem.

After processing each frame, the sensor calibration module 290 obtains alist of checkerboard-to camera transforms {T_(i)}. Since the camera wasnever moved during the entire sequence, the sensor calibration module290 uses the list of transforms to measure the checkerboard movementbetween any pair of frames. Given any checkerboard point X in 3Dcheckerboard coordinate in frame i, the sensor calibration module 290determines the projected position of the point X in frame j using theequation X′_(j)=T_(j) ⁻¹T_(i)X_(i). If there is no movement, thenT_(i)=T_(j) and the two cancel out resulting in X′_(j)=X_(i). The sensorcalibration module 290 determines the amount of movement by thedifference between X′_(j) and its actual position in) frame j as givenby d(X,i,j)=|X′_(j)−X_(j)|=|T_(j) ⁻¹T_(i)X_(i)−X_(j)|. Given twodetected checkerboard patterns, each comprising a list of checkerboardcorners, C={X_(i)}, the sensor calibration module 290 determines theirmovement by using the equation m(C,i,j)=max_(X=C)d(X,i,j).

Thus, the sensor calibration module 290 uses a modified greedy algorithmto still walk all frames, and select a frame if and only if its movementis small compared to its neighbors: m(C,i,i−1)<x, m(C,i,i+1)<x and itsmovement is large compared to existing selections: m(C,i,s)>y, s∈{S},where e.g., x=1 cm and y=20 cm.

Accordingly, the sensor calibration module 290 executes the followingsteps (as illustrated in FIG. 11): (1) Select still frames forcalibration. In this step, the sensor calibration module 290 selects allthe static views of the checkerboard. (2) Run the first pass oflidar-to-camera calibration, using nearby checkerboard patterns only.Step 2 bootstraps initial lidar-to-camera calibration by using frames inwhich checkerboard is near the car (e.g., within 3 meters from thelidar). In this scenario the system does not need to rely on any priorknowledge (such as a rough lidar-to-camera transform) to locate thecheckerboard in 3d, because the checkerboard points would naturally fitthe dominant plane within the 3 meter radius. The sensor calibrationmodule 290 then computes an initial lidar-to-camera calibration bysolving the PnP problem using the small subset of static views. (3) Runthe second pass of lidar-to-camera calibration, using results fromprevious step as initial estimate, and use checkerboard patterns fromall frames to refine the transform. In step 3, the sensor calibrationmodule 290 relies on the initial lidar-to-camera transform, and may usestereo triangulation to robustly locate the checkerboard in 3d even whenit is far away from the car. This allows the sensor calibration module290 to use all the static frames to optimize the lidar-to-cameratransform and get the final output.

Calibration Based on Reflective Tape

In some embodiments the HD map system performs calibration using onevideo (calibration sequence) with a checkerboard, and a second video(test sequence) with a board with a static tape pattern based onreflective tape on it, which is more suitable for visualizingcalibration error. Accordingly, the process comprises a first video withcalibration sequence of frames with the checkerboard pattern and asecond video with a test sequence of frames with reflective tape basedpattern.

The calibration sequence includes frames with checkerboard patternplaced at various spots including a set S1 of spots in a close range infront of the car, i.e., within a threshold distance, a set S2 of spotswithin medium range, i.e., greater than a first threshold but less thana second threshold, a set of spots in a far range, i.e., range greaterthan a threshold distance. For example, the HD map system receives avideo with about 25 spots, about 5 in front of the car (within 3 metersto the lidar), about 10 in the medium range (about 6 meters away fromthe lidar), and about 10 in the far range (about 10 meters away from thelidar). For each layer, the spots are equally spaced out to cover theentire shared field of vision. The checkerboard is fully contained byboth views to be useful, so the checkerboard needs to move in smallersteps near the boundary of shared field of vision, to ensure that itcovers the shared field of vision as much as possible. If thecheckerboard is partially outside of a camera view, that frame isdropped, so having more frames will not hurt calibration, just wastemore time.

The HD system assumes that the checkerboard is held about 45 degreesangled to the ground, with left side higher than the right side (fromholder's point of view). The way the checkerboard is held determines thepre-measured offsets of the pattern on the checkerboard.

The test sequence is based on a black board with reflective tapes on it.The strong contrast of intensity near tape boundaries makes it moresuitable for visualizing calibration error. To simulate the usage oflidar-to-camera calibration, the board is held by the side of the car atdifferent distances (to simulate traffic sign projection), and laid onthe ground in front of the car (to simulate lane line and crosswalkprojection).

In an embodiment, the HD map system shows debug images upon completionof the calibration process and test process showing lidar pointsoverlaid on top of left and right images, color-coded by intensity sothat a user can inspect the alignment between lidar points and imagepixels. The HD map system displays a user interface that allows the userto select lidar points and corresponding image pixels (e.g.,corresponding to the same tape boundary). The HD map system receives theuser selection and measures the 3d distance (in cm). The HD map systemensures that the error does not exceed a threshold (half OMap cell) inall views.

FIG. 18A shows a test sequence based on a striped pattern according toan embodiment. As shown, a black board is held with reflective tapearound the car to simulate traffic signs. The board is laid on theground in front of the car to simulate lane lines. The board is kept atvarying distances including close (less than a predetermined threshold)and far (greater than a predetermined threshold). FIG. 18B shows sampledebug images for a test sequence, according to an embodiment.

Static Tape Pattern

In some embodiments, the test sequence is replaced with a static setupin the calibration environment (for example, garage). There are verticalstripes of reflective tape on the wall facing the vehicle, and possiblyon the side walls to cover the horizontal field of vision as much aspossible (to simulate traffic signs). There is reflective tape on theground in front of the car (to simulate lane lines and crosswalks). Thereflective tape is different in color with background material (e.g.,dark color tapes for white wall) so that they can be easilydifferentiated from images.

Similar to the test sequence, the HD map system checks the alignment foreach reflective tape between their lidar projection and image pixels.There will be a viewer to facilitate error estimation, which allows theuser to slightly tweak one of the 6 degrees of freedom in thecalibration matrix to achieve better alignment between image and lidarprojection. By the amount of tweaking (e.g., 0.1 degree change in pitch,0.1 degree in yaw and 2 cm in x), the HD map system estimates the errorin the calibration matrix accordingly.

FIGS. 19A-B show example setups of reflective tapes. FIG. 19A shows atop-down view of a reflective tape pattern on the ground, according toan embodiment. Tapes are put in front of the car within camera field ofview (specified by dashed lines). The tapes have a different color fromthe ground so that they can be easily distinguished from camera view.Tapes are made of a material that has very different reflectivity fromthe ground so that tape boundary can be easily distinguished from lidarpoint cloud. Embodiments can use different patterns and the selection ofthe exact pattern can vary and preknown measurements are not needed. Thepattern should (1) fill camera field of view as much as possible, and(2) provide constraints in all 6 degree of freedoms. E.g., if onlyvertical tapes are placed on the ground (parallel to the car direction),there will be little constraint to tx (translation in car direction) ofthe transform. Similarly, if only horizontal tapes are placed on theground (orthogonal to the car direction), there will be littleconstraint to ty (translation orthogonal to car direction). Embodimentsuse tapes in at least two diagonal directions to ensure that error inany of the 6 DoF can be spotted as misalignment somewhere in thepattern.

FIG. 19B shows a front view of the reflective tape pattern on the wall,according to an embodiment. Similar to FIG. 19A, the pattern is notstrict and preknown measurement is not needed. However, the patternshould (1) fill camera field of view as much as possible—which means apattern may be needed on the wall to both sides of the car as well as infront of the car, and (2) provide constraints in all 6 degree offreedoms.

Placement of Checkerboard Pattern

In an embodiment, the checkerboard pattern (or any other pattern) usedfor calibration of sensors is manually moved by a person. A user mayview the sensor data to determine whether the various portions of thearea viewed by the sensor are covered by the different places where thecheckerboard pattern is placed. If certain portion of the viewing areais not covered, there is a likelihood that the calibration is notaccurate. For example, if the checkerboard is mostly placed on the lefthalf of the viewing area and there is no placement in the right half ofthe viewing area, the sensor calibration may not be accurate.

In an embodiment, the HD map system detects the presence of thecheckerboard pattern in sensor data and determines coordinates of thecorners of the checkerboard pattern. The HD map system maintains a shapeof the overall viewing area of sensor. The HD map system and overlaysthe areas where the checkerboard pattern occurs in images and lidarscans that are processed based on the determined coordinates of thecheckerboard corners. Accordingly, the HD map system determines regionsof the viewing area that have not yet been covered.

In an embodiment, the HD map system determines portions of the viewingare that are not yet covered by iteratively moving a templaterepresenting the checkerboard pattern within the shape represented bythe viewing area and determining whether the new area covered by thetemplate includes a substantial portion of viewing area that has notbeen covered so far by the placements of checkerboard pattern.Accordingly, the HD map system iteratively determines various positionsfor the checkerboard pattern in the viewing area and maps them to alocation and orientation of the checkerboard pattern in the real world.

The HD map system provides a position and orientation for the nextplacement of the checkerboard pattern, for example, by specifying adistance from the vehicle where the checkerboard pattern should beplaced and an orientation, for example, whether it should be laid on theground, held vertically, and if the pattern has stripes, whether thestripes should be at an incline pointing top left to bottom right orfrom bottom left to top right. In an embodiment, the HD map systemprovides real time direction to a person holding the checkerboardpattern, whether the person should move away from the vehicle, towardsthe vehicle, tilt the pattern appropriately, and so on.

The direction may be provided via an application, for example, a clientapplication executing on a mobile device. The HD map system maintainsvarious variables including an amount of the portion of the viewing areathat has been covered, an amount of left boundary that has been covered,an amount of right boundary that has been covered, an amount of bottomboundary that has been covered, an amount of top boundary that has beencovered, and so on. The HD map system evaluates these variables anddetermines the directions for the next placement of the pattern forsending to the client device of a user managing the placements.

In an embodiment, the HD map system presents a heat map via a userinterface, such that the heat map shows an indication of how well eachportion of the viewing area of the sensor is covered. In an embodiment,the HD map system presents multiple heat maps, for example, one heat mapfor a close placement of the pattern and another heat map for a distantplacement of the pattern. Accordingly, the HD map system presents aplurality of heat maps, each heat map for a different depth value.

In an embodiment, instead of a heat map, the HD map system presents auser interface that divides the viewing area into different portions andassociates each portion with a score indicating the amount of coveragevia the pattern for that portion. For example, a low score valueindicates less coverage and high score value indicates higher coverage.

FIG. 20 shows a flowchart illustrating the process of determining aplacement of the checkerboard pattern, according to an embodiment. Thecheckerboard pattern placement module 930 initializes a shaperepresenting the viewing area of the sensors. The shape may be specifiedusing the lidar coordinates or any other 3D coordinate system. Thecheckerboard pattern placement module 930 repeats the following steps2020, 2030, 2040, 2050, and 2060 of the process. The checkerboardpattern placement module 930 receives sensor data based on a placementof the checkerboard pattern, for example, the most recent placement ofthe checkerboard pattern. In an embodiment, the placement is specifiedusing the depth and orientation of the checkerboard. Alternatively, theplacement is specified by identifying coordinates of a plurality ofcorners. The checkerboard pattern placement module 930 determines 2030the coordinates of the checkerboard pattern based on the placement. Forexample, the checkerboard pattern placement module 930 determinescoordinates of all the corners of the checkerboard pattern.

The checkerboard pattern placement module 930 updates 2040 informationdescribing the portions of the viewing area that are covered by theplacements of the checkerboard pattern processed so far. Determineportions of viewing area that are already covered by the checkerboardpattern. The checkerboard pattern placement module 930 identifies 2050the next position of the checkerboard pattern to cover a portion of theviewing area that is not yet covered by the placements of thecheckerboard pattern processed so far. The checkerboard patternplacement module 930 sends instructions re next placement of thecheckerboard pattern based on the position of the identified portion ofthe viewing area. In an embodiment, the checkerboard pattern placementmodule 930 displays a user interface displaying the position of theportion of the viewing area that needs to be covered next. In anotherembodiment, the checkerboard pattern placement module 930 gives realtime instructions directing a user to move so as to align thecheckerboard pattern held by the user with the identified position. Inanother embodiment, the checkerboard pattern is automatically positionedusing a drone. The checkerboard pattern is attached to a drone and theHD map system sends instructions to the drone using an API (applicationprogramming interface) of the drone to move the drone to the identifiedposition. The HD map system repeats the above instructions until allportions of the viewing area are covered. In an embodiment, the HD mapsystem repeats the entire process of covering the viewing area for aplurality of depths, for example, for a close-up position of thecheckerboard pattern and for a distant position of the checkerboardpattern. In an embodiment, the vehicle is driven to a facility that haspreviously placed patterns at various locations including close to thevehicle and at distant locations. The facility uses automatic or manualmechanical devices such as arms to move the checkerboard patterns indifferent positions. For example, an arm places the checkerboard patternin front of the vehicle and close to the vehicle. The arm removes thecheckerboard pattern from the front of the vehicle. A different armplaces one or more checkerboard patterns at a distant location from thevehicle. The process is repeated to get full coverage of the viewingarea.

Calibration Based on 3D-to-3D Transform

In one embodiment, a client application of the HD map system, forexample, a point cloud viewer displays a scene based on the point cloud.The user interface of the client application allows users to selectpoints of the point cloud, for example, using a pointing device such asa mouse based cursor. The client application receives from the user,selection of three corners of the checkerboard in the point cloud. TheHD map system refines the corner locations based on intensity alignment.The HD map system thereby obtains a set of 3d checkerboard corners inthe lidar coordinate.

From the same frame, the HD map system detects checkerboard pattern inthe left and right images and triangulates 3d checkerboard corners inthe camera coordinate. With a set of corresponding points in the twocoordinate system, the system determines a least squares solution forthe rigid transform between lidar and camera coordinates. In someembodiments, this process receives the coordinates of corners frommultiple frames. The HD map system uses RANSAC in the rigid transformsolver to account for noise in the checkerboard corners detected fromlidar points. Embodiments of the invention provide an improvement byautomating the detection of checkerboard corners from lidar points (thatachieves higher precision by separating plane fitting from intensitybased refinement), and using PnP solver for 3d-to-2d correspondences,which avoids the error in stereo triangulation.

Edgel Based Calibration of Sensors

The edgel based calibration module 950 performs lidar-to-cameracalibration by detecting edgels in both lidar based point cloud andcamera based images, and optimizing the alignment between those edges.An edgel corresponds to edges representing boundaries of objects orshapes in an image, for example, objects or shapes representing edges ofbuildings, traffic signs, poles, figures painted on the road such asturn arrows, lane lines, and so on. The edgel based calibration module950 obtains 3D points from the lidar scan and 2D points from the image.

These embodiments are advantageous since they can be used for performingcalibration of sensors using real world data representing objects/imagesthat are obtained by a vehicle driving on the road. Accordingly, theseembodiments can be used to perform calibration without requiring use ofcalibration object, for example, a checkerboard or requiring use of acontrolled environment meant specifically for calibration.

The HD map system achieves higher calibration accuracy by using frameswhere the car is stopped at intersections, to prevent other sources oferror (e.g., pose error) from affecting calibration. One advantage ofthese solutions is that they are capable of online calibration duringdriving of the vehicle. In some scenarios, due to the high variance inreal world data, the process may not converge all the time, and mayresult in lower precision even when the process does converge.

Calibration parameters drift over time, either caused by vehicle shakingor material expansion due to heat. For data collection, test vehicles,or research vehicles, performing calibration on a regular basis may notbe very inconvenient since the number of times the calibration isperformed is not very high. However, if there is a big fleet of vehiclesor if there is a large number of commercial vehicles that are operating,requiring all these vehicles to be calibrated in a controlledenvironment is time consuming as well as resource consuming. Also,requiring a controlled environment places burden on the user of thevehicle to take time and resources for performing calibration.

However, embodiments of the invention perform online calibration byrefining calibration parameters using real time data while the car isdriving. As a result, the process does not require a large number ofvehicles to be calibrated in controlled setting, thereby providingsignificant savings in terms of time and resources.

FIG. 21 illustrates the overall process for performing calibration ofsensors of a vehicle based on edgel detection, according to anembodiment. The edgel based calibration module 950 receives 2110 a lidarscan captured by the lidar of the vehicle and a camera image captured bya camera of the vehicle. The lidar scan and the camera image areobtained from a frame captured at the same time. Accordingly, the lidarscan and the camera image substantially represent the same scene orsurroundings of the vehicle or at least have a significant overlap inthe portion of the scene captured by the lidar and the camera. If thereis a time difference between the capture of the lidar scan and thecamera image, the edgel based calibration module 950 performs a temporalcorrection, for example, by transforming the 3D points to a positioncorresponding to the time of capture of the image.

The edgel based calibration module 950 determines 2120 a set S1 of edgesfrom the camera image by processing the pixels of the camera image. Theset S1 of edges may be determines by an edge detection technique, forexample, a gradient based edge detection technique, a Laplacian basededge detection technique, or a neural network based edge detectiontechnique. In an embodiment, the edgel based calibration module 950detects edges in the camera image by identifying changes ordiscontinuity in image brightness. The edgel based calibration module950 identifies points at which image brightness changes sharply andidentifies a set of curved line segments termed edges passing throughthe identified points.

The edgel based calibration module 950 further determines a set S2 ofedges from the lidar scan. The edgel based calibration module 950determines an edge in the lidar scan based on depth discontinuities inthe 3D points of the lidar scan as well as intensity discontinuities inthe 3D points of the lidar scan. The edgel based calibration module 950measures intensity discontinuities for identifying edges based on pointson the ground and uses depth discontinuities to identify edges based onpoints that are above the ground. FIG. 22 further illustrates theprocess for determining edges in the lidar scan.

The edgel based calibration module 950 receives 2140 a transform T1 fortransforming between 3D points of lidar scan and 2D points of cameraimage. In an embodiment, the transform is 6-dimensional transformbetween the two sensors, i.e., the lidar and the camera. Specifically,the six values are the x, y, and z translations, and the roll, pitch,and yaw Euler angle rotations between the two sensors. In an embodiment,the transform operation between lidar and camera sensors comprises thefollowing steps. The first step transforms 3D points from lidarcoordinates to 3D points in camera coordinates (both in 3D) usingequation X_(camera)=T_(lidar2camera)*X_(lidar) where T_(lidar2camera) isthe 6 DoF transform between lidar and camera coordinates. The next stepprojects points in camera coordinate into two dimensional image spaceusing equation x=P*X_(camera) where P is the 3×4 projection matrix ofthe camera, encoded by focal length and principal point position.

The edgel based calibration module 950 determines 2150 pairs of matchingedges between S1 and S2 based on transform T1. The edgel basedcalibration module 950 determines 2160 a transform T2 that is moreaccurate than T1 by iterative improvement of the transform T1. The edgelbased calibration module 950 2160 initializes the second transform tothe first transform. The edgel based calibration module 950 iterativelymodifies the second transform, such that an aggregate distance betweenthe corresponding edges of the one or more pairs of matching edges basedon the second transform in a current iteration is less than an aggregatedistance between the edges of the one or more pairs of matching edgesbased on the second transform in a previous iteration.

The edgel based calibration module 950 uses the transform T2 for varioussteps of HD map generation for example, to combine lidar scan data withthe image data. The generated HD map may be used for various purposes,for example, for guiding an autonomous vehicle.

Techniques for calibration of sensors of a vehicle are described in thearticle titled Automatic Online Calibration of Cameras and Lasers,Stanford Artificial Intelligence Laboratory, co-authored by J. Levison,S. Thrun, which is incorporated herein by reference in its entirety. Theprocess disclosed in this reference only uses off-ground points, becauseground points, by definition, are continuous and never exhibit rangediscontinuity. The HD map system in contrast includes points from theground, by also detecting points at intensity discontinuity, i.e., whenneighboring points have a large delta in intensity. The method starts bysegmenting the lidar point cloud into ground and off-ground points, byfitting a ground plane. For off-ground points, the HD map system followLevinson's algorithm. For ground points, the HD map system usesintensity discontinuity instead of range discontinuity. The systemcombines range and intensity discontinuity scores in a linear fashion,where weights are adjusted such that typical vertical features (e.g.,silhouette of a pole) have the same weight towards optimization as atypical ground feature (e.g., lane line boundaries). It improves theaccuracy and robustness of calibration, specifically in the dimensionsin which the vehicle is driving and pitch, which are poorly constrainedby techniques that use off-ground points alone.

FIG. 22 illustrates the process for processing the ground pointsseparate from the remaining points for performing calibration of sensorsof a vehicle based on edgel detection, according to an embodiment.

The edgel based calibration module 950 determines 2210 a ground plane inthe point cloud corresponding to the lidar scan. In an embodiment, theedgel based calibration module 950 determines a plane passing through aset of points that are immediately in front of the vehicle. For example,the edgel based calibration module 950 identifies a set of points thatare in the lowest portion of the lidar scan representing the portion ofthe scene immediately in front of the vehicle and passes a plane throughthe set of points.

The edgel based calibration module 950 identifies 2220 based on theground plane, a set S1 of 3D points on ground and a set S2 of 3D pointsthat are above the ground, for example, 3D points representingbuildings, traffic signs, and so on. Accordingly, the edgel basedcalibration module 950 separates the 3D points on the ground from the 3Dpoints above the ground so that the two sets of points can be processedseparately. The edgel based calibration module 950 determines edgeswithin the set of point S1 representing ground using lidar intensityvalues, for example, by identifying sudden change or discontinuity inintensity values while travelling in a particular direction along thepoint cloud represented by the lidar scan. Accordingly, the edgel basedcalibration module 950 determines 2230 S1′ a subset of set S1representing points associated with greater than a threshold change inintensity. The edgel based calibration module 950 determines a change inintensity by measuring the gradient of the intensity values in theneighborhood of each point. The edgel based calibration module 950determines a change in depth by measuring the gradient of the depthvalues in the neighborhood of each point.

The edgel based calibration module 950 determines edges within the setof point S2 representing 3D points above ground using lidar depthvalues, for example, by identifying sudden change or discontinuity indepth values while travelling in a particular direction along the pointcloud represented by the lidar scan. Accordingly, the edgel basedcalibration module 950 determines 2240 S2′ a subset of set S2representing points associated with greater than a threshold change indepth. The edgel based calibration module 950 uses intensity fordetermining edges on ground since there is no depth variation for pointson ground, unlike points above ground. Therefore, the edgel basedcalibration module 950 uses features on ground such as letters writtenon the road, e.g., stop, yield, and such words on the road, shapes offigures drawn on the road, for example, left turn arrow, right turnarrow, lane lines, and so on. These features are associated withintensity change and not depth change. For example, a letter written onthe road may be painted in white, and the edges of the shape of theletter have a change in intensity from high intensity of the white paintto low intensity of the asphalt of the road that is adjacent to theletter. For structures above the ground, the edgel based calibrationmodule 950 uses change in depth. For example, a pole may be at a depthof 10 meters and the structure behind the pole may be a building that is20 meters away. As a result, edgel based calibration module 950determines edges associated with the pole using a set of pointsassociated with a change in depth from 10 meters to 20 meters.

The edgel based calibration module 950 determines 2250 edges based onthe points in sets S1′ and S2′. The edgel based calibration module 950identifies sets of points from the set S1′ or set S2′ that are close toeach other and determining edges representing curves or likes passingthrough the identified plurality of points. The edgel based calibrationmodule 950 identifies sets of points that are close to each other byperforming a clustering algorithm, for example, k-means clustering.

In an embodiment, the edgel based calibration module 950 determines anedge score representing a degree of confidence with which the pointcorresponds to an edge. The edgel based calibration module 950determines the edge score for each point above ground based on adifference between the depth of the point and adjacent point.Accordingly, higher edge score represents higher difference is depth andis indicative of a higher confidence that the point corresponds to anedge. The edgel based calibration module 950 determines the edge scorefor each point on the ground based on a difference between the intensityof the point and adjacent point. Accordingly, higher edge scorerepresents higher difference in intensity and is indicative of a higherconfidence that the point on the ground corresponds to an edge. Sincethe edge scores of points on the ground are determined using a differentmechanism compared to the edge scores of points above ground, edgelbased calibration module 950 normalizes the two scores so that they arecomparable value. In an embodiment, the edgel based calibration module950 determines a distribution of the edge scores of points on theground, and a distribution of the edge scores of points above ground.The edgel based calibration module 950 determines an aggregate value v1representing the edge scores of points on the ground and an aggregatevalue v2 representing the edge scores of points above the ground. Theaggregate value may be determined as a median value, a maximum value, amean value or using another measure of statistical aggregate. The edgelbased calibration module 950 scales the edge scores of at least one ofthe sets of points based on the values v1 and v2. For example, the edgelbased calibration module 950 scales the edge scores of ground points byv2/v1 or scales the edge scores of above ground points by v1/v2.

In an embodiment, the HD map system performs a full (i.e., exhaustive) 6degrees of freedom (DoF) search instead of a simple gradient descent.Gradient descent often converges to a bad solution, especially when theoptimal solution is several search steps away from the starting point(which is almost always the case). Doing a full search guarantees anoptimal solution within a small neighborhood in the 6 DoF search space.The edgel based calibration module 950 uses a fairly close initialestimate (e.g., the calibration results from two weeks back) to ensurethat a small neighborhood is sufficient for determining a solution viaan exhaustive search. For example, the HD map system may searches 1degree for raw, pitch, yaw and 10 centimeters in translation x, y, andz.

In an embodiment, the edgel based calibration module 950 determines thesize of the neighborhood in which the exhaustive search is performedbased on the rate at which the calibration is performed. The edgel basedcalibration module 950 determines the size of the neighborhood in whichthe exhaustive search is performed as a value that is inversely relatedto the rate at which the calibration is performed. Accordingly, if theedgel based calibration module 950 performs calibration more frequently(i.e., initial estimate is more accurate), the edgel based calibrationmodule 950 reduces the size of the search neighborhood. This is sobecause the initial estimate of the transform that is improved is moreaccurate if the HD map system uses a recently performed calibrationresult as the initial transform estimate.

FIG. 23 illustrates the process of searching for an improved transformbased on an initial transform, according to an embodiment. The edgelbased calibration module 950 determines 2310 an upper bound and a lowerbound for each transformation parameter based on historical data. Thetransform has a plurality of transform parameters, each transformparameter corresponding to a dimension, for example, six transformparameters, roll/pitch/yaw and three x, y, and z translations tx/ty/tz.In an embodiment, the edgel based calibration module 950 determines theupper and lower bounds for each transform parameter based on an amountof variation in the value of the transform parameter based on historicaldata, for example, recent history based on driving routes along whichthe vehicle was driven recently. For example, a particular camera maynot have been installed properly and is loose along a particulardirection, there having more movement along that dimension resulting inhigher drift along that dimension. As a result, if the value of thattransform parameter computed in the previous iteration that performedcalibration was used as the initial transform, the transform parameteris likely to have more drift compared to another transform parameter.Each transform parameter may have distinct upper and lower bounds. Forexample, a transform parameter t1 may have upper and lower bounds l1 andu1 respectively, whereas a transform parameter t2 may have upper andlower bounds l2 and u2 respectively where l1 is distinct from l2 and u1is distinct from u2.

The selection of bounds for each parameters ensures that the edgel basedcalibration module 950 does not perform unnecessary search, for example,searching along a dimension for more than a threshold delta value evenif the transform parameter corresponding to that dimension is unlikelyto change more than a delta value that is much smaller than thethreshold delta value. Accordingly, the ability to select the requiredbounds for each transform parameters makes the process of performingexhaustive search efficient.

The edgel based calibration module 950 initializes 2320 transformparameters to values that were previously determined, for example, whenthe sensors were calibrated the last time (i.e., the most recenttransform parameter values). The edgel based calibration module 950determines an alignment score for each transform based on a degree ofmatch between the edges determined using the different sensors, i.e.,lidar and camera. Accordingly, a better alignment score is indicative ofhigher degree of match between the edges.

The edgel based calibration module 950 performs an exhaustive search forthe best transform within the polygon formed by the determined bounds.The exhaustive search divides the polygon formed by the determinedbounds into smaller portions and determines a value of the transformparameters for a point corresponding to each portion. The edgel basedcalibration module 950 determines the alignment score for each point andselects the transform parameters corresponding to the point with thebest alignment score.

In an embodiment, the edgel based calibration module 950 uses aniterative modification based approach that modifies the transform byvarying the value of one of the transform parameters and recomputes thealignment score for the modified transform. The edgel based calibrationmodule 950 varies a transform parameter by adding a delta value orsubtracting a delta value from the transform parameter. The delta valuefor each parameter may be preconfigured. The edgel based calibrationmodule 950 varies each transform parameter in each iteration anddetermines the alignment scores for the total number of combinations oftransforms obtained by varying each transform parameter. The edgel basedcalibration module 950 selects the combination of transform parametersthat has the highest alignment score, thereby representing the currentbest alignment between edges determined using the two sensors. The edgelbased calibration module 950 repeats this computation by treating thecurrent best transform as an initial transform and varying the transformparameters again. The edgel based calibration module 950 selects thebest transform that results in the highest alignment score correspondingto the best alignment.

In an embodiment, the edgel based calibration module 950 combines theiterative modification based approach and the exhaustive search basedapproach. For example, the edgel based calibration module 950 initiallyperforms the edgel based calibration module 950 to get closer to thesolution and then switches to exhaustive search based approach to findthe solution. In an embodiment, the edgel based calibration module 950switches to the exhaustive search based approach if an aggregate measurebased on the alignment scores of the edges reaches above a thresholdvalue.

Besides real-world data representing scenes surrounding a vehicle duringnormal driving of a vehicle, the proposed techniques for performingedgel based calibration can also be applied to a controlled environment(e.g., a garage) decorated with calibration objects (e.g., reflectivetapes on the walls and ground). The techniques do not assume any priorknowledge of scene structure, as long as there are range and/orintensity boundaries in the lidar points, and corresponding edgels inthe images.

In an embodiment, to detect parameter drift over time, the HD map systemuses an application comprising a user interface acting as a viewerinstalled in the vehicle. A user, for example, a driver of the vehicleviews images shown in the viewer displaying reflective tapes in theenvironment and checks if point projection looks good. The applicationdisplays widgets that receive input from the user for modifyingroll/pitch/yaw and tx/ty/tz by small amounts to improve point-to-pixelalignment. The application quantifies the amount of drift based on thereceived user input and sends an alert for re-calibration if needed.

Computing Machine Architecture

FIG. 24 is a block diagram illustrating components of an example machineable to read instructions from a machine-readable medium and executethem in a processor (or controller). Specifically, FIG. 24 shows adiagrammatic representation of a machine in the example form of acomputer system 2400 within which instructions 2424 (e.g., software) forcausing the machine to perform any one or more of the methodologiesdiscussed herein may be executed. In alternative embodiments, themachine operates as a standalone device or may be connected (e.g.,networked) to other machines. In a networked deployment, the machine mayoperate in the capacity of a server machine or a client machine in aserver-client network environment, or as a peer machine in apeer-to-peer (or distributed) network environment.

The machine may be a server computer, a client computer, a personalcomputer (PC), a tablet PC, a set-top box (STB), a personal digitalassistant (PDA), a cellular telephone, a smartphone, a web appliance, anetwork router, switch or bridge, or any machine capable of executinginstructions 2424 (sequential or otherwise) that specify actions to betaken by that machine. Further, while only a single machine isillustrated, the term “machine” shall also be taken to include anycollection of machines that individually or jointly execute instructions2424 to perform any one or more of the methodologies discussed herein.

The example computer system 2400 includes a processor 2402 (e.g., acentral processing unit (CPU), a graphics processing unit (GPU), adigital signal processor (DSP), one or more application specificintegrated circuits (ASICs), one or more radio-frequency integratedcircuits (RFICs), or any combination of these), a main memory 2404, anda static memory 2406, which are configured to communicate with eachother via a bus 2408. The computer system 2400 may further includegraphics display unit 2410 (e.g., a plasma display panel (PDP), a liquidcrystal display (LCD), a projector, or a cathode ray tube (CRT)). Thecomputer system 2400 may also include alphanumeric input device 2412(e.g., a keyboard), a cursor control device 2414 (e.g., a mouse, atrackball, a joystick, a motion sensor, or other pointing instrument), astorage unit 2416, a signal generation device 2418 (e.g., a speaker),and a network interface device 2420, which also are configured tocommunicate via the bus 2408.

The storage unit 2416 includes a machine-readable medium 2422 on whichis stored instructions 2424 (e.g., software) embodying any one or moreof the methodologies or functions described herein. The instructions2424 (e.g., software) may also reside, completely or at least partially,within the main memory 2404 or within the processor 2402 (e.g., within aprocessor's cache memory) during execution thereof by the computersystem 2400, the main memory 2404 and the processor 2402 alsoconstituting machine-readable media. The instructions 2424 (e.g.,software) may be transmitted or received over a network 2426 via thenetwork interface device 2420.

While machine-readable medium 2422 is shown in an example embodiment tobe a single medium, the term “machine-readable medium” should be takento include a single medium or multiple media (e.g., a centralized ordistributed database, or associated caches and servers) able to storeinstructions (e.g., instructions 2424). The term “machine-readablemedium” shall also be taken to include any medium that is capable ofstoring instructions (e.g., instructions 2424) for execution by themachine and that cause the machine to perform any one or more of themethodologies disclosed herein. The term “machine-readable medium”includes, but not be limited to, data repositories in the form ofsolid-state memories, optical media, and magnetic media.

Additional Configuration Considerations

The foregoing description of the embodiments of the invention has beenpresented for the purpose of illustration; it is not intended to beexhaustive or to limit the invention to the precise forms disclosed.Persons skilled in the relevant art can appreciate that manymodifications and variations are possible in light of the abovedisclosure.

For example, although the techniques described herein are applied toautonomous vehicles, the techniques can also be applied to otherapplications, for example, for displaying HD maps for vehicles withdrivers, for displaying HD maps on displays of client devices such asmobile phones, laptops, tablets, or any computing device with a displayscreen. Techniques displayed herein can also be applied for displayingmaps for purposes of computer simulation, for example, in computergames, and so on.

Some portions of this description describe the embodiments of theinvention in terms of algorithms and symbolic representations ofoperations on information. These algorithmic descriptions andrepresentations are commonly used by those skilled in the dataprocessing arts to convey the substance of their work effectively toothers skilled in the art. These operations, while describedfunctionally, computationally, or logically, are understood to beimplemented by computer programs or equivalent electrical circuits,microcode, or the like. Furthermore, it has also proven convenient attimes, to refer to these arrangements of operations as modules, withoutloss of generality. The described operations and their associatedmodules may be embodied in software, firmware, hardware, or anycombinations thereof.

Any of the steps, operations, or processes described herein may beperformed or implemented with one or more hardware or software modules,alone or in combination with other devices. In one embodiment, asoftware module is implemented with a computer program productcomprising a computer-readable medium containing computer program code,which can be executed by a computer processor for performing any or allof the steps, operations, or processes described.

Embodiments of the invention may also relate to an apparatus forperforming the operations herein. This apparatus may be speciallyconstructed for the required purposes, and/or it may comprise ageneral-purpose computing device selectively activated or reconfiguredby a computer program stored in the computer. Such a computer programmay be stored in a tangible computer readable storage medium or any typeof media suitable for storing electronic instructions, and coupled to acomputer system bus. Furthermore, any computing systems referred to inthe specification may include a single processor or may be architecturesemploying multiple processor designs for increased computing capability.

Embodiments of the invention may also relate to a computer data signalembodied in a carrier wave, where the computer data signal includes anyembodiment of a computer program product or other data combinationdescribed herein. The computer data signal is a product that ispresented in a tangible medium or carrier wave and modulated orotherwise encoded in the carrier wave, which is tangible, andtransmitted according to any suitable transmission method.

Finally, the language used in the specification has been principallyselected for readability and instructional purposes, and it may not havebeen selected to delineate or circumscribe the inventive subject matter.It is therefore intended that the scope of the invention be limited notby this detailed description, but rather by any claims that issue on anapplication based hereon.

1. A non-transitory computer readable storage medium storinginstructions for performing calibration of sensors of a vehicle, whereinthe instructions when executed by a processor, cause the processor toperform the steps including: receiving a first lidar scan of a firstview comprising a pattern, the first lidar scan captured by a lidarmounted on an autonomous vehicle, wherein the pattern is positioned lessthat a first threshold distance from the autonomous vehicle; receiving afirst camera image of the first view, the first camera image captured bya camera mounted on the autonomous vehicle; determining an approximatelidar-to-camera transform based on the first lidar scan of the firstview and the first camera image of the first view; receiving a secondlidar scan of a second view comprising the pattern, the second lidarscan captured by the lidar mounted on the autonomous vehicle, whereinthe pattern is positioned greater than a second threshold distance fromthe autonomous vehicle; receiving, by a camera mounted on the autonomousvehicle, a second camera image of the second view; determining anaccurate lidar-to-camera transform based on the location of the patternin the second lidar scan and the location of the pattern in the cameraimage of the second view; receiving sensor data comprising imagesreceived from the camera and lidar scans from the lidar; generating ahigh definition map based on the sensor data using the accuratelidar-to-camera transform; and storing the high definition map in acomputer readable storage medium for use in navigating the autonomousvehicle.
 2. The non-transitory computer readable storage medium of claim1, wherein the instructions when executed by the processor, furthercause the processor to perform steps including: sending signals to thecontrols of the autonomous vehicle based on the high definition map. 3.The non-transitory computer readable storage medium of claim 1, whereininstructions for determining the accurate lidar-to-camera transformcomprises instructions for: detecting location of the pattern in thecamera image of the second view; and determining points on the patternin the second lidar scan based on the approximate lidar-to-cameratransform and the points on the pattern in the image of the second view.4. The non-transitory computer readable storage medium of claim 1,wherein the camera is a left camera, and the camera image is a leftcamera image, wherein the instructions when executed by the processor,further cause the processor to perform steps including: receiving, by aright camera mounted on the autonomous vehicle, a right camera image ofthe second view; for a plurality of points on the pattern: detecting afirst location of the point from the left camera image; detecting asecond location of point from the right camera image; triangulating thefirst location and the second location to obtain a 3D location of thepoint in camera coordinates; and determining 3D coordinates of the pointby applying the approximate lidar-to-camera transform to the 3D locationof the point; fitting a dominant plane within the plurality of points;and adjusting locations of one or more 3D points by projecting the oneor more 3D points to the dominant plane.
 5. The non-transitory computerreadable storage medium of claim 1, wherein the instructions whenexecuted by the processor, further cause the processor to perform stepsincluding: determining a bounding polygon of the pattern using thecamera image; projecting a set of 3D points of lidar scan onto thecamera image; identifying a subset of 3D points of the lidar scan suchthat a projected point corresponding to each of the subset of 3D pointsis within a threshold of the bounding polygon; fitting a dominant planewithin the subset of 3D points; and adjusting locations of one or more3D points by projecting the one or more 3D points to the dominant plane.6. The non-transitory computer readable storage medium of claim 1,wherein the instructions when executed by the processor, further causethe processor to perform steps including: monitoring portions of viewingarea of at least one of the camera or lidar that are covered by thepattern in sensor data captured; and determining a portion of theviewing area that is not covered by the pattern in sensor data captured;and determining the position of the pattern for subsequently capturingsensor data based on the portion of the viewing area that is not coveredby the pattern.
 7. The non-transitory computer readable storage mediumof claim 6, wherein the instructions when executed by the processor,further cause the processor to perform steps including: transmittinginformation describing the location for placing the patterncorresponding to the determined position.
 8. The non-transitory computerreadable storage medium of claim 1, wherein the instructions whenexecuted by the processor, further cause the processor to perform stepsincluding: determining position of points on the lidar scan based onintensity data in the lidar scan.
 9. A method for performing calibrationof sensors of a vehicle, the method comprising: receiving a first lidarscan of a first view comprising a pattern, the first lidar scan capturedby a lidar mounted on an autonomous vehicle, wherein the pattern ispositioned less that a first threshold distance from the autonomousvehicle; receiving a first camera image of the first view, the firstcamera image captured by a camera mounted on the autonomous vehicle;determining an approximate lidar-to-camera transform based on the firstlidar scan of the first view and the first camera image of the firstview; receiving a second lidar scan of a second view comprising thepattern, the second lidar scan captured by the lidar mounted on theautonomous vehicle, wherein the pattern is positioned greater than asecond threshold distance from the autonomous vehicle; receiving, by acamera mounted on the autonomous vehicle, a second camera image of thesecond view; determining an accurate lidar-to-camera transform based onthe location of the pattern in the second lidar scan and the location ofthe pattern in the camera image of the second view; receiving sensordata comprising images received from the camera and lidar scans from thelidar; generating a high definition map based on the sensor data usingthe accurate lidar-to-camera transform; and storing the high definitionmap in a computer readable storage medium for use in navigating theautonomous vehicle. 10-14. (canceled)
 15. A computer system comprising:one or more processors; and a non-transitory computer readable storagemedium storing instructions for performing calibration of sensors of avehicle, wherein the instructions when executed by a processor, causethe processor to perform the steps including: receiving a first lidarscan of a first view comprising a pattern, the first lidar scan capturedby a lidar mounted on an autonomous vehicle, wherein the pattern ispositioned less that a first threshold distance from the autonomousvehicle; receiving a first camera image of the first view, the firstcamera image captured by a camera mounted on the autonomous vehicle;determining an approximate lidar-to-camera transform based on the firstlidar scan of the first view and the first camera image of the firstview; receiving a second lidar scan of a second view comprising thepattern, the second lidar scan captured by the lidar mounted on theautonomous vehicle, wherein the pattern is positioned greater than asecond threshold distance from the autonomous vehicle; receiving, by acamera mounted on the autonomous vehicle, a second camera image of thesecond view; determining an accurate lidar-to-camera transform based onthe location of the pattern in the second lidar scan and the location ofthe pattern in the camera image of the second view; receiving sensordata comprising images received from the camera and lidar scans from thelidar; generating a high definition map based on the sensor data usingthe accurate lidar-to-camera transform; and storing the high definitionmap in a computer readable storage medium for use in navigating theautonomous vehicle. 16-20. (canceled)